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Guo, Qiwei, Yayong Chen, Yu Tang, et al. "Lychee Fruit Detection Based on Monocular Machine Vision in Orchard Environment." Sensors 19, no. 19 (2019): 4091. http://dx.doi.org/10.3390/s19194091.
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Due to the change of illumination environment and overlapping conditions caused by the neighboring fruits and other background objects, the simple application of the traditional machine vision method limits the detection accuracy of lychee fruits in natural orchard environments. Therefore, this research presented a detection method based on monocular machine vision to detect lychee fruits growing in overlapped conditions. Specifically, a combination of contrast limited adaptive histogram equalization (CLAHE), red/blue chromatic mapping, Otsu thresholding and morphology operations were adopted to segment the foreground regions of the lychees. A stepwise method was proposed for extracting individual lychee fruit from the lychee foreground region. The first step in this process was based on the relative position relation of the Hough circle and an equivalent area circle (equal to the area of the potential lychee foreground region) and was designed to distinguish lychee fruits growing in isolated or overlapped states. Then, a process based on the three-point definite circle theorem was performed to extract individual lychee fruits from the foreground regions of overlapped lychee fruit clusters. Finally, to enhance the robustness of the detection method, a local binary pattern support vector machine (LBP-SVM) was adopted to filter out the false positive detections generated by background chaff interferences. The performance of the presented method was evaluated using 485 images captured in a natural lychee orchard in Conghua (Area), Guangzhou. The detection results showed that the recall rate was 86.66%, the precision rate was greater than 87% and the F1-score was 87.07%.
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Yustiantara, Natanael Putra. "IMAGE ENHACEMENT PADA CITRA GESTUR TANGAN MENGGUNAKAN CONTRAST LIMITED ADAPTIVE HISTOGRAM EQUALIZATION." Joutica 6, no. 2 (2021): 454. http://dx.doi.org/10.30736/jti.v6i2.612.
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Image Enhacement merupakan proses perbaikan kualitas citra yang dilakukan dengan menggunakan beberapa metode. Citra yang paling sering dilakukan perbaikan kualitas adalah citra digital. Citra digital sering digunakan pada pengolahan citra biometrik, pengenalan wajah, pengenalan tanda tangan, bahkan permasalahan pada Closed Circuit Television (CCTV). Penelitian ini bertujuan untuk memberikan perbedaan hasil proses image enhacement pada gambar yang telah tertangkap oleh CCTV. Penelitian ini menggunakan 3 buah metode yaitu, Histogram Equalization (HE), Adaptive Histogram Equalization (AHE), dan Contrast Limited Adaptive Histogram Equalization (CLAHE) untuk melakukan perbaikan citra, sedangkan objek yang akan digunakan pada penelitian ini adalah citra gesture tangan. Dari hasil penelitian ini dapat dilihat bahwa Nilai MSE (Mean Squared Error) yang mendekati angka 0 adalah gambar yang menggunakan metode CLAHE (Contrast Limited Adaptive Histogram Equalization) dengan nilai sebesar 653.5. Untuk nilai PSNR (Peak Signal to Noise Ratio) sendiri nilai yang paling besar yaitu 29.9783476895 dengan menggunakan metode CLAHE.
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Mohd-Isa, Wan-Noorshahida, Joel Joseph, Noramiza Hashim, and Nbhan Salih. "Enhancement of digitized X-ray films using Contrast-Limited Adaptive Histogram Equalization (CLAHE)." F1000Research 10 (October 15, 2021): 1051. http://dx.doi.org/10.12688/f1000research.73236.1.
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Background: Rural clinics still have X-ray facilities that produce physical films, which are sent to the nearest hospital for evaluation. Purchasing digitalization facilities is costly, thus, sending digitized films to the radiologist may be a solution. This can be achieved via digital photo capture. However, there can be different output resolutions that may not be optimized for online diagnosis. This paper investigates if digitized X-ray films can be enhanced using image processing techniques of Contrast-Limited Adaptive Histogram Equalization (CLAHE), Normalized-CLAHE (N-CLAHE) and Min-Max Normalized-CLAHE (MMCLAHE). Methods: We collected and digitized 21 X-ray films with low, medium, and high resolutions and implemented the CLAHE, N-CLAHE and MMCLAHE image enhancement. These methods introduced a limit to clip the histogram of image intensities so as to reduce any noise amplification before file compression with the Fast Fourier Transform (FFT) and Discrete Cosine Transform (DCT). Quantitative metrics of the Peak Signal-to-Noise Ratio (PSNR) and Mean-Squared Error (MSE) were used to compare the accuracies between digitized and processed X-ray films. A qualitative evaluation was performed by a medical practitioner to validate the accuracy of enhanced digitized X-ray. Results: It had been found that both CLAHE and MMCLAHE provided good average PSNR values of 31dB - 32dB and produced low MSE values compared to N-CLAHE. The results of qualitative evaluation attained 89.9% correct diagnosis on nine randomly selected images. Generally, the evaluation indicated that the results fulfill the acceptable criteria for further evaluation and there seemed to be no pathological differences observed. Conclusion: This paper presented a proof of concept on an implementation of the CLAHE technique and its variations on digitized X-ray films. This paper had shown potential improvements with the proposed enhancement methods that may contribute to an increase efficiency in healthcare processes at rural clinics.
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Abood, Loay Kadom. "Contrast enhancement of infrared images using Adaptive Histogram Equalization (AHE) with Contrast Limited Adaptive Histogram Equalization (CLAHE)." Iraqi Journal of Physics (IJP) 16, no. 37 (2018): 127–35. http://dx.doi.org/10.30723/ijp.v16i37.84.
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The objective of this paper is to improve the general quality of infrared images by proposes an algorithm relying upon strategy for infrared images (IR) enhancement. This algorithm was based on two methods: adaptive histogram equalization (AHE) and Contrast Limited Adaptive Histogram Equalization (CLAHE). The contribution of this paper is on how well contrast enhancement improvement procedures proposed for infrared images, and to propose a strategy that may be most appropriate for consolidation into commercial infrared imaging applications.The database for this paper consists of night vision infrared images were taken by Zenmuse camera (FLIR Systems, Inc) attached on MATRIC100 drone in Karbala city. The experimental tests showed significant improvements.
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GuruKesavaDasu, Dr Gopisetty. "Local Adaptive Image Equalization." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem29906.
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This paper presents a comprehensive approach to image enhancement, targeting the enhancement of contrast and reduction of noise in digital images. Leveraging state-of-the-art algorithms, the proposed methodology encompasses a strategic pipeline. Initially, the images undergo Histogram Equalization, a fundamental technique, to globally enhance contrast. Building upon this foundation, Contrast Limited Adaptive Histogram Equalization (CLAHE) is applied to achieve localized contrast enhancement, ensuring optimal balance and preservation of image details. Furthermore, the Adaptive Gamma Correction with Weighting Distribution (AGCWD) algorithm is integrated to fine-tune the enhanced images, dynamically adjusting gamma values to suppress noise and amplify visual features. The implementation harnesses Python with OpenCV and Flask frameworks, facilitating seamless integration and accessibility. Through rigorous experimentation and comparative analysis, the efficacy of the proposed approach is demonstrated, showcasing substantial improvements in image quality and fidelity. The findings underscore the practical utility and efficacy of the proposed image enhancement system, positioning it as a valuable tool for various real-world applications in image processing and computer vision domains. Keywords: Image Enhancement ,Noise Reduction ,Histogram Equalization, CLAHE, Adaptive Gamma Correction, OpenCV Library, Flask Web Framework , Image Fidelity.
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Suharyanto, Suharyanto, and Frieyadie Frieyadie. "ANALISIS KOMPARASI PERBAIKAN KUALITAS CITRA BAWAH AIR BERBASIS KONTRAS PEMERATAAN HISTOGRAM." INTI Nusa Mandiri 15, no. 1 (2020): 95–102. http://dx.doi.org/10.33480/inti.v15i1.1501.
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Dalam makalah ini, penulis melakukan komparasi metode pemerataan histogram yang biasa digunakan untuk meningkatkan citra. Gambar bawah air umumnya mengalami penurunan kontras yang cukup rendah dan kualitas bayangan yang menurun. Saat kita melakukan penangkapan gambar dari udara ke air, keseluruhan gambar akan mengalami perubahan. Selama menangkap beberapa efek absorpsi, refleksi dan hamburan diinduksi dalam bentuk kontras, kualitas, dan noise saat gambar terlihat tidak jelas atau kabur. Ini membuat gambar dipenuhi satu bayangan. Untuk mengatasi faktor-faktor tersebut dan penggunaan sumber daya bawah air maka peningkatan citra diperlukan. Maka dalam makalah ini, mengusulkan menggunakan metode untuk peningkatan citra bawah air berbasis pemerataan histogram yaitu Histogram Equalization (HE), Adaptive Histogram Equalization (AHE) dan Contrast Limited Adaptive Histogram Equalization (CLAHE). Penelitian ini melakukan komparasi metode pemerataan histogram dengan tujuan untuk mengetahui kinerja metode HE, AHE, CLAHE dalam meningkatkan kualitas gambar bawah air. Berdasarkan kinerja hasil pengukuran menggunakan Mean Square Error (MSE), dan Peak Signal-to-Noise Ratio (PSNR) terjadi peningkatan kualitas gambar bawah air setelah di tingkatkan menggunakan CLAHE lebih baik daripada AHE dan HE.
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Fousia, M. Shamsudeen, and Raju G. "A novel equalization scheme for the selective enhancement of optical disc and cup regions and background suppression in fundus imagery." TELKOMNIKA Telecommunication, Computing, Electronics and Control 17, no. 4 (2019): 1715–22. https://doi.org/10.12928/TELKOMNIKA.v17i4.5364.
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The ratio of the diameters of Optic Cup (OC) and Optic Disc (OD), termed as ‘Cup to Disc Ratio’ (CDR), derived from the fundus imagery is a popular biomarker used for the diagnosis of glaucoma. Demarcation of OC and OD either manually or through automated image processing algorithms is error prone because of poor grey level contrast and their vague boundaries. A dedicated equalization which simultaneously compresses the dynamic range of the background and stretches the range of ODis proposed in this paper. Unlike the conventional GHE, in the proposed equalization, the original histogram is inverted and weighted nonlinearly before computing the Cumulative Probability Density (CPD). The equalization scheme is compared with Adaptive Histogram Equalization (AHE), Global Histogram Equalization (GHE) and Contrast Limited Adaptive Histogram Equalization (CLAHE) in terms of the difference between the mean grey levels of OD and the background, using a quantitative metric known as Contrast Improvement Index (CII). The CII exhibited by CLAHE, GHE and the proposed scheme are 1.1977 ± 0.0326, 1.0862 ± 0.0304 and 1.3312 ± 0.0486, respectively.The proposed method is observed to be superior to CLAHE, GHE and AHE and it can be employed in Computerized Clinical Decision Support Systems (CCDSS) to improve the accuracy of localizing the OD and the computation of CDR.
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Andrikevych, S. A., and S. Yu Tuzhanskyi. "Improved method of adaptive histogram equalization for color fundus images." Optoelectronic Information-Power Technologies 49, no. 1 (2025): 82–88. https://doi.org/10.31649/1681-7893-2025-49-1-82-88.
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The paper investigates the improvement of the visualization quality of color fundus images using the contrast-limited adaptive histogram equalization (CLAHE) method. The method is applied to the R, G, B channels of images from the HRF database. The results showed an increase in the average contrast, and visual analysis confirmed better visibility of fundus vessels while preserving local details. The proposed approach is effective for image preprocessing in medical diagnostics. The proposed CLAHE method by separately processing the R, G, B channels has demonstrated its effectiveness in enhancing the contrast of fundus images, as evidenced by an increase in the average contrast by 4.4% and better visibility of retinal vessels, especially in the green channel, and also helps to make abnormal structures such as neoplasms or hemorrhages more visible. However, the method causes a shift in the color balance, which may affect the diagnostic value of the images, and also enhances chromatic aberration at its borders.
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Ng, Yu Jie, and Kok Swee Sim. "A Review of Brain Early Infarct Image Contrast Enhancement Using Various Histogram Equalization Techniques." International Journal on Advanced Science, Engineering and Information Technology 14, no. 6 (2024): 1849–60. https://doi.org/10.18517/ijaseit.14.6.10115.
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Stroke is one of the leading causes of death worldwide, accounting for five of all deaths in Malaysia. It happens when an infarct from a blocked blood artery results in brain necrosis. Diagnoses involving brain diseases and injuries can be made with the help of CT scans, which create axial images by using exact X-ray measurements. These scans offer vital information on the anatomy and physiology of the brain. For an appropriate diagnosis, early infarct brain CT scan contrast can be improved. The two main types of histogram equalization (HE) approaches used for this purpose are Global Histogram Equalization (GHE) and Local Histogram Equalization (LHE), which is also referred to as adaptive histogram equalization (AHE). Locally, LHE uses the block overlapped method to improve photos. Additional sophisticated methods include Dualistic Sub Image Histogram Equalization (DSIHE), Contrast Limited Adaptive Histogram Equalization (CLAHE), Recursive Sub Image Histogram Equalization (RSIHE), Gamma Correction Adaptive Extreme Level Eliminating With Weighting Distribution (GCAELEWD), and Brightness Preserving Bi Histogram Equalization (BBHE). The contrast of brain images is greatly improved by these techniques. Nevertheless, a number of these methods have issues with blur, noise, and preserving local image brightness. According to our research, CLAHE and DSIHE are especially good to improve image contrast and yield better outcomes than other techniques. These methods lessen frequent problems, which makes them better suited to create precise and comprehensive brain images—an essential component of successful stroke diagnosis and treatment.
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Yu, Cheng Yi, Hsueh Yi Lin, and Cheng Jian Lin. "Image Contrast Enhancement by Hybrid 3SAIHT and CLAHE Algorithm." Applied Mechanics and Materials 479-480 (December 2013): 870–77. http://dx.doi.org/10.4028/www.scientific.net/amm.479-480.870.
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Human visual perception is insensitive to certain shades of gray but can distinguish among 20 to 30 shades of gray under a given adaptation level. In this paper, we propose an image fusion pipeline that generates a high vision quality image by fusing the Three-Scale Adaptive Inverse Hyperbolic Tangent (3SAIHT) and the Contrast-Limited Adaptive Histogram Equalization (CLAHE) algorithms to increase detail and edge information. Fusion results are clearer and better with regard to display quality and contrast enhancement.
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Khan, Sajid Ali, Shariq Hussain, and Shunkun Yang. "Contrast Enhancement of Low-Contrast Medical Images Using Modified Contrast Limited Adaptive Histogram Equalization." Journal of Medical Imaging and Health Informatics 10, no. 8 (2020): 1795–803. http://dx.doi.org/10.1166/jmihi.2020.3196.
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The low contrast medical images seriously affect the clinical diagnosis process. To improve the image quality, we propose an effective medical images contrast enhancement technique in this paper. Shear wavelet transformation is used for decomposition of image components into low-frequency and high-frequency. The low-frequency part contrast is adjusted by applying modified contrast limited adaptive histogram equalization (CLAHE). The resultant image is further processed through technique of fuzzy contrast enhancement to maintain the spectral information of an image. Results of the experimentation show that our proposed technique enhance the image contrast up to a good degree while preserving the image details.
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Nuraisha, Safira, and Sri Handayani. "ANALISIS IMPLEMENTASI CONTRAST LIMITED ADAPTIVE HISTOGRAM EQUALIZATION (CLAHE) UNTUK DETEKSI CITRA SIDIK JARI TIRUAN." Djtechno Jurnal Teknologi Informasi 2, no. 1 (2021): 38–44. http://dx.doi.org/10.46576/djtechno.v2i1.1255.
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Autentikasi biometrik dengan sidik jari paling sering digunakan untuk sistem keamanan atau autentikasi sebuah akun. Seiring dengan berkembangnya model sistem keamanan menggunakan autentikasi sidik jari, muncul masalah baru yaitu penggunaan sidik jari Penggunaan sidik jari palsu dapat dilakukan melalui scanner sidik jari yang menerima salinan dari sidik jari asli yang sering disebut dengan artificial fingerprints. Penggunaan sidik jari palsu dapat mengancam keamanan dari sebuah sistem. Permasalahan deteksi sidik jari dan identifikasi bahan yang dapat meniru karakteristik sidik jari diperburuk oleh dua hal, pertama, sensor standar tidak mampu membedakan citra dari sidik jari asli dan sidik jari replika. Kedua, seringkali tidak ada isyarat yang jelas bahwa citra tersebut berasal dari sidik jari replika atau dengan kata lain sidik jari replika yang sangat mirip dengan sidik jari asli sehingga sulit untuk dibedakan. Penelitian ini bertujuan untuk mendeteksi citra sidik jari tiruan dengan tingkat akurasi yang tinggi. Dataset yang digunakan merupakan dataset publik ATVS. Metode yang diusulkan yaitu ekstraksi fitur citra sidik jari dengan kontras GLCM (Gray Level Co-Occurance Matrix) dengan metode peningkatan kualitas citra CLAHE (Contrast Limited Adaptive Histogram Equalization). Hasil deteksi citra sidik jari menggunakan CLAHE menghasilkan akurasi yang lebih baik dibandingkan tanpa menggunakan CLAHE
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Nasir, Ahmad Lutfi Afifi Mohd, Roslan Umar, Wan Nural Jawahir Wan Yussof, et al. "Comparative Analysis of Image Processing Technique in Determining the New Crescent Moon Visibility." Journal of Physics: Conference Series 2915, no. 1 (2024): 012004. https://doi.org/10.1088/1742-6596/2915/1/012004.
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Abstract This research presents a comparative analysis of advanced image processing techniques to enhance the visibility of the new crescent moon, a crucial element in astronomy and the lunar calendar. The primary objective is to assess the effectiveness of Contrast Adjustment (CA), Histogram Equalization (HE), Contrast Limited Adaptive Histogram Equalization (CLAHE), and Gamma Correction (GC) in improving new crescent moon visibility. The study utilized a comprehensive dataset of new crescent moon images captured on various dates and times, with each image undergoing a specific image processing technique. The findings revealed that CLAHE markedly outperforms the other methods, offering superior contrast enhancement and detailed visibility. This suggests that CLAHE is the most proficient technique for augmenting new crescent moon visibility, thereby providing critical insights for future astronomical observations and practical applications. The research significantly advances the refinement of new crescent moon observation techniques, contributing to both scientific understanding and the enhancement of practical implementation in the field.
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Tinaliah, Tinaliah, and Triana Elizabeth. "Peningkatan Kualitas Citra X-Ray Paru-Paru Pasien Covid-19 Menggunakan Metode Contrast Limited Adaptive Histogram Equalization." Jurnal Teknologi Informasi 4, no. 2 (2020): 345–49. http://dx.doi.org/10.36294/jurti.v4i2.1709.
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Abstract - Covid-19 is currently a pandemic around the world, and until now there has been no specific cure for this disease. An x-ray examination of the lungs is one of the tests that can be done to detect Covid-19. X-ray results must be read carefully to determine whether the patient is really exposed to Covid-19. Improved quality of x-ray images is needed to help doctors or health practitioners see more clearly the x-ray results of the lungs. One of the methods used to improve image quality is the CLAHE method. This method is a simple and efficient method to implement and is able to produce a better image than the original, unprocessed image. Based on the test results, it can be concluded that the CLAHE method can improve the quality of x-ray images of the lungs. The CLAHE method with the Rayleigh distribution has the lowest average MSE value of 0.01474 and has the highest average PSNR value of 18.6642 dB compared to the Uniform distribution and the Exponential distribution.Keywords - Covid-19, Lung X-ray, Image Enhancement, and CLAHE. Abstrak – Covid-19 saat ini telah menjadi pandemi di seluruh dunia. Obat khusus untuk penyakit covid-19 sampai saat ini belum ditemukan. Covid-19 dapat dideteksi dengan melakukan pemeriksaan menyeluruh, yaitu salah satunya dengan menggunakan pemeriksaan x-ray paru-paru. Pembacaan hasil x-ray harus dilakukan secara teliti untuk menentukan apakah pasien benar terkena Covid-19. Untuk membantu dokter ataupun praktisi kesehatan dalam lebih jelas untuk melihat hasil x-ray paru-paru, maka dibutuhkan peningkatan kualitas citra x-ray agar hasil kualitas citra dapat lebih baik dan lebih jelas dibaca. Metode CLAHE merupakan metode yang dapat digunakan untuk peningkatan kualitas citra. Metode ini merupakan metode yang simpel dan efisien untuk diimplementasi, serta dapat menghasilkan citra yang lebih baik. Berdasarkan hasil pengujian dapat disimpulkan bahwa dengan menerapkan metode CLAHE hasil kualitas citra x-ray paru-paru dapat lebih ditingkatkan. Nilai rata – rata MSE Metode CLAHE dengan distribusi Rayleigh mempunyai nilai rata – rata yang paling rendah sebesar 0.01474, dan mempunyai nilai rata – rata PSNR yang paling tinggi sebesar 38.6642 dB dibandingkan distribusi Uniform dan distribusi Exponential.Kata Kunci - Covid-19, X-ray Paru-Paru, Peningkatan Kualitas Citra, dan CLAHE.
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Kenyta, Claudia, and Daniel Martomanggolo Wonohadidjojo. "Perbandingan Performa Histogram Equalization untuk Peningkatan Kualitas Gambar Minim Cahaya pada Android." Ultimatics : Jurnal Teknik Informatika 12, no. 2 (2020): 80–88. http://dx.doi.org/10.31937/ti.v12i2.1667.
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When the photos are taken in low light condition, the quality of the results will not meet their expectation. Image Enhancement method can be used to enhance the quality of the photos taken in low light condition. One of the algorithms used is called Histogram Equalization (HE), that works using Histogram basis. The superiority of HE algorithm in enhancing the quality of the photos taken in low light condition is the simplicity of the algorithm itself and it does not need a high specification device for the algorithm to run. One variant of HE algorithm is Contrast Limited Adaptive Histogram Equalization (CLAHE). This paper shows the implementation of HE algorithm and its performance in enhancing the quality of photos taken in low light condition on Android based application and the comparison with CLAHE algorithm. The results show that, HE algorithm is better than CLAHE algorithm.
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Wanling, Wu, and Noraisyah Mohamed Shah. "Fundus Image Enhancement using CLAHE." Journal of New Explorations in Electrical Engineering 1, no. 1 (2025): 67–78. https://doi.org/10.22452/nece.vol1no1.6.
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Fundus retinal images are crucial for ophthalmologists to diagnose diseases and monitor changes in the condition. However, due to factors such as lighting conditions, instrument effects, and individual differences, fundus images often have the drawbacks of low contrast and lack of details. To improve the quality and accuracy of images, contrast enhancement technology for fundus images has become a research hotspot. This paper proposes a new CLAHE (Contrast Limited Adaptive Histogram Equalization) method to improve the brightness and contrast of retinal images. The method improves the luminosity of fundus images by using gamma correction in the HSV color space and enhances the contrast of images by limiting contrast histogram equalization in the L*a*b* color space. Finally, the effectiveness of the method is verified through the STARE dataset. The results show that compared with the traditional CLAHE method in the RGB color space and the WAHE method, the method proposed in this paper has better improvement effects on color retinal images, and performs well in adaptability, color fidelity, local detail preservation, and algorithm implementation simplicity, making it suitable for fundus image processing under different lighting conditions. It is also easy to deploy and use in practical applications, providing reference and guidance for researchers and healthcare professionals.
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Sari, Ni Larasati Kartika, Maria Oktavianti, and Samsun Samsun. "Analisis Karakter Segmen Abnormal pada Citra Mamografi dengan Menggunakan Berbagai Metode Preprocessing Citra." Jurnal Ilmiah Giga 22, no. 1 (2020): 1. http://dx.doi.org/10.47313/jig.v22i1.737.
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Penelitian ini menganalisis pengaruh penerapan beberapa jenis algoritma preprocessing untuk mencari karakteristik segmen abnormal yang tampak pada citra mamografi. Mamografi merupakan pemeriksaan radiografi khusus payudara. Penerapan algoritma preprocessing yang terdiri dari metode filtering, contrast enhancement, sharpening, dan smoothing diharapkan dapat mengurangi noise dan meningkatkan kontras citra mamografi serta membantu ahli radiologi untuk melakukan diagnosis pada citra. Pada penelitian ini akan digunakan dua algoritma filtering yaitu median filter dan gaussian filter. Selain itu digunakan dua algoritma contrast enhancement yaitu global histogram equalization dan CLAHE (Contrast Limited Adaptive Histogram Equalization). Nilai piksel rata-rata segmen abnormal berkisar antara 206.9-213.3 dan rasio sumbu minor/mayor segmen abnormal berkisar antara 0.5-0.7.Pemilihan jenis metode filter (median filter dan gaussian filter) tidak mempengaruhi hasil nilai piksel rata-rata maupun rasio sumbu minor/mayor dan ukuran segmen abnormal, namun pemilihan jenis metode peningkatan kontras (CLAHE dan global histogram equalization) menghasilkan segmen abnormal dengan ukuran yang berbeda. Metode global histogram equalization menghasilkan segmen abnormal yang tidak dapat dibedakan dengan sekitarnya sehingga hasil ekstrasi segmen terlalu besar.
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Werdiningsih, Indah, Ira Puspitasari, and Rimuljo Hendradi. "Recognizing Daily Activities of Children with Autism Spectrum Disorder Using Convolutional Neural Network Based on Image Enhancement." Cybernetics and Information Technologies 25, no. 1 (2025): 78–96. https://doi.org/10.2478/cait-2025-0005.
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Abstract Independence for individuals with disabilities, Children with Autism Spectrum Disorder (ASD), need skills to perform daily activities. This study focuses on recognizing the daily activities of children with ASD using a Convolutional Neural Network (CNN) based on augmented images. The CNN architectures employed are Visual Geometry Group 19 (VGG19) and MobileNetV2, while image improvement techniques include Histogram Equalization, Contrast Stretching, and Contrast Limited Adaptive Histogram Equalization (CLAHE). The data consists of eating (606 videos) and drinking (477 videos) activities recorded by therapists or parents. CLAHE proved the most effective, achieving an SSIM of 0.998 and a PSNR of 38.466 for the eating activities, an SSIM of 0.998, and a PSNR of 38.296 for the drinking activities. Experimental results using CLAHE and VGG19 showed a recognition model accuracy of 85%, while VGG19 without image enhancement achieved an accuracy of 83%. CNN with image enhancement achieves slightly better accuracy, though the difference is insignificant.
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Juslan, Wulandari, and Alva Hendi Muhammad. "Evaluasi Kinerja Metode Peningkatan Kontras (CLAHE & HE) pada Klasifikasi Ras Kucing menggunakan VGG16." Edumatic: Jurnal Pendidikan Informatika 9, no. 1 (2025): 246–55. https://doi.org/10.29408/edumatic.v9i1.29578.
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Cat breed classification is challenging in image processing due to complex visual variations from crossbreeding, which affect care requirements. This study evaluates the effectiveness of Contrast Limited Adaptive Histogram Equalization (CLAHE) and Histogram Equalization (HE) in cat breed classification using a VGG16-based Convolutional Neural Network (CNN). The dataset consists of 4,656 cat images from six breeds, processed with CLAHE and HE for contrast enhancement before training. It is divided into 70% for training, 15% for validation, and 15% for testing. The model is trained for 10 epochs using the Adam optimizer, a 0.0001 learning rate, and batch sizes of 16, 32, and 64. Evaluation using accuracy, precision, recall, and F1-score shows that CLAHE achieves the highest accuracy (99.39%), surpassing HE (99.17%) by 3.29%. CLAHE is more effective in preserving local details, improving precision (78.67%), recall (78.33%), and F1-score (78%). The highest performance is in the Sphinx breed (F1-score 92%), while the lowest is in American Shorthair (F1-score 72%). A high standard deviation indicates classification variations across breeds, but CLAHE consistently improves model accuracy. These findings suggest that CLAHE is more effective than HE in enhancing cat breed classification and offers a more efficient solution than adopting a complex model architecture.
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Saifullah, Shoffan. "ANALISIS PERBANDINGAN HE DAN CLAHE PADA IMAGE ENHANCEMENT DALAM PROSES SEGMENASI CITRA UNTUK DETEKSI FERTILITAS TELUR." Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) 9, no. 1 (2020): 134. http://dx.doi.org/10.23887/janapati.v9i1.23013.
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Perkembangan teknologi di bidang peternakan mampu memberikan kemudahan dalam proses penetasan ayam. Namun, proses deteksi fertilitas telur telah diperiksa secara manual oleh pekerja yang menyortir telur yang fertil dan infertil. Penelitian ini bertujuan untuk mempermudah proses pendeteksian gambar fertilitas telur menggunakan sistem komputerisasi secara otomatis. Deteksi fertilitas telur dilakukan preprocessing dengan metode Image Enhancement. Dalam metode ini, metode Histogram Equalization (HE) dan metode Contrast Limited Adaptive Histogram Equalization (CLAHE) dibandingkan satu sama lain pada proses peningkatan citra (Image Enhancement). HE memberikan hasil yang dapat mengidentifikasi fertilitas telur. Namun, ada satu faktor penting dalam pemrosesan gambar, yaitu pengambilan telur (proses akuisisi). Proses deteksi fertilitas telur menggunakan segmentasi dengan metode morfologi. Proses pengujian yang dilakukan menggunakan metode kekauratan pada metode HE dan CLAHE yang masing-masing adalah sebesar 96% dan 79%. Hasil menunjukkan bahwa hasil HE lebih jelas terlihat dibandingkan dengan CLAHE.
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Liu, Chengwei, Xiubao Sui, Xiaodong Kuang, Yuan Liu, Guohua Gu, and Qian Chen. "Adaptive Contrast Enhancement for Infrared Images Based on the Neighborhood Conditional Histogram." Remote Sensing 11, no. 11 (2019): 1381. http://dx.doi.org/10.3390/rs11111381.
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In this paper, an adaptive contrast enhancement method based on the neighborhood conditional histogram is proposed to improve the visual quality of thermal infrared images. Existing block-based local contrast enhancement methods usually suffer from the over-enhancement of smooth regions or the loss of some details. To address these drawbacks, we first introduce a neighborhood conditional histogram to adaptively enhance the contrast and avoid the over-enhancement caused by the original histogram. Then the clip-redistributed histogram of the contrast-limited adaptive histogram equalization (CLAHE) is replaced by the neighborhood conditional histogram. In addition, the local mapping function of each sub-block is updated based on the global mapping function to further eliminate the block artifacts. Lastly, the optimized local contrast enhancement process, which combines both global and local enhanced results is employed to obtain the desired enhanced result. Experiments are conducted to evaluate the performance of the proposed method and the other five methods are introduced as a comparison. Qualitative and quantitative evaluation results demonstrate that the proposed method outperforms the other block-based methods on local contrast enhancement, visual quality improvement, and noise suppression.
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Saifullah, Shoffan. "Segmentasi Citra Menggunakan Metode Watershed Transform Berdasarkan Image Enhancement Dalam Mendeteksi Embrio Telur." Systemic: Information System and Informatics Journal 5, no. 2 (2020): 53–60. http://dx.doi.org/10.29080/systemic.v5i2.798.
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Image processing dapat diterapkan dalam proses deteksi embrio telur. Proses deteksi embrio telur dilakukan dengan menggunakan proses segmentasi, yang membagi citra sesuai dengan daerah yang dibagi. Proses ini memerlukan perbaikan citra yang diproses untuk memperoleh hasil optimal. Penelitian ini akan menganalisis deteksi embrio telur berdasarkan image processing dengan image enhancement dan konsep segmentasi menggunakan metode watershed transform. Image enhacement pada preprocessing dalam perbaikan citra menggunakan kombinasi metode Contrast Limited Adaptive Histogram Equalization (CLAHE) dan Histogram Equalization (HE). Citra grayscale telur diperbaiki dengan menggunakan metode CLAHE, dan hasilnya diproses kembali dengan menggunakan HE. Hasil perbaikan citra menunjukkan bahwa metode kombinasi CLAHE-HE memberikan gambar secara jelas daerah objek citra telur yang memiliki embrio. Proses segmentasi dengan menggunakan konversi citra ke citra hitam putih dan segmentasi watershed mampu menunjukkan secara jelas objek telur ayam yang memiliki embrio. Hasil segmentasi mampu membagi daerah telur memiliki embrio secara nyata dan akurat dengan persentase sebesar » 98%.
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Wu, Shibin, Shaode Yu, Yuhan Yang, and Yaoqin Xie. "Feature and Contrast Enhancement of Mammographic Image Based on Multiscale Analysis and Morphology." Computational and Mathematical Methods in Medicine 2013 (2013): 1–8. http://dx.doi.org/10.1155/2013/716948.
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A new algorithm for feature and contrast enhancement of mammographic images is proposed in this paper. The approach bases on multiscale transform and mathematical morphology. First of all, the Laplacian Gaussian pyramid operator is applied to transform the mammography into different scale subband images. In addition, the detail or high frequency subimages are equalized by contrast limited adaptive histogram equalization (CLAHE) and low-pass subimages are processed by mathematical morphology. Finally, the enhanced image of feature and contrast is reconstructed from the Laplacian Gaussian pyramid coefficients modified at one or more levels by contrast limited adaptive histogram equalization and mathematical morphology, respectively. The enhanced image is processed by global nonlinear operator. The experimental results show that the presented algorithm is effective for feature and contrast enhancement of mammogram. The performance evaluation of the proposed algorithm is measured by contrast evaluation criterion for image, signal-noise-ratio (SNR), and contrast improvement index (CII).
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Dwika Putra, Erwin, Ermatita Ermatita, and Abdiansah Abdiansah. "Handwritten Kaganga script classification using deep learning and image fusion." Bulletin of Electrical Engineering and Informatics 14, no. 2 (2025): 1290–97. https://doi.org/10.11591/eei.v14i2.8747.
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Classification of traditional handwriting script and to preserve many cultures have been developed in some parts of the world, including image classification of handwriting Kaganga script. This study aims to propose a new combination model by implementing top-hat transform (THT) and contrast-limited adaptive histogram equalization (CLAHE) with discrete wavelet transform (DWT) to support the performance of the convolutional neural network (CNN) in Kaganga script classification. The top-hat transform and contrast-limited adaptive histogram equalization with discrete wavelet transform Fusion L2 convolutional neural network (DWT-THCL L2 CNN) models get the best accuracy from the CNN with L1 regularization, CNN with dropout regularization, CNN with L2 regularization and CNN with L2 regularization and CLAHE models. Based on the experimental results, the DWT-THCL L2 CNN model successfully increased training accuracy by 7.76%, validation accuracy by 5.11%, and testing accuracy by 3.73% from the CNN L1 model. The DWT-THCL L2 CNN model received a training accuracy of 99.87%, validation accuracy of 82.61%, and testing accuracy of 82.61%, while the CNN model with L1 regularization (L1 CNN) only received a training accuracy of 92.11%, validation accuracy of 77.50%, and testing accuracy of 78.88%.
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Kurniawan, Muhammad Bayu, and Ema Utami. "COMPARATIVE ANALYSIS OF CONTRAST ENHANCEMENT METHODS FOR CLASSIFICATION OF PEKALONGAN BATIK MOTIFS USING CONVOLUTIONAL NEURAL NETWORK." Jurnal Teknik Informatika (Jutif) 5, no. 6 (2024): 1779–87. https://doi.org/10.52436/1.jutif.2024.5.6.2621.
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Batik artists in Pekalongan have freedom in determining motifs, creating a diversity of distinctive batik motifs. However, this diversity often makes it difficult for people to recognize the different motifs, as visual identification requires in-depth knowledge. The lack of understanding about Pekalongan batik is a challenge in recognizing these motifs. To overcome this challenge, an efficient and accurate method of motif identification is needed. This study aims to analyze the efficacy of contrast enhancement methods in improving the classification results of Pekalongan batik motifs using convolutional neural networks (CNN) with ResNet50 architecture. The dataset of 480 images was collected directly from Museum Batik Pekalongan and split into three distinct categories: 15% for validation, 15% for testing, and 70% for training. Two contrast enhancement methods: contrast limited adaptive histogram equalization (CLAHE) and histogram equalization (HE), were applied to create additional datasets. The Adam optimizer was used to train the model over 50 epochs at a learning rate of 0.001. The test results show that the original dataset contrast-enhanced with CLAHE reaches the best accuracy of 83%, followed by the original dataset contrast-enhanced with HE at 81%, and the original dataset at 76%. This finding shows that the application of contrast enhancement methods, especially CLAHE, can increase the model's accuracy in classifying batik motifs.
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Ma, Jinxiang, Xinnan Fan, Simon X. Yang, Xuewu Zhang, and Xifang Zhu. "Contrast Limited Adaptive Histogram Equalization-Based Fusion in YIQ and HSI Color Spaces for Underwater Image Enhancement." International Journal of Pattern Recognition and Artificial Intelligence 32, no. 07 (2018): 1854018. http://dx.doi.org/10.1142/s0218001418540186.
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To improve contrast and restore color for underwater images without suffering from insufficient details and color cast, this paper proposes a fusion algorithm for different color spaces based on contrast limited adaptive histogram equalization (CLAHE). The original color image is first converted from RGB space to two different spaces: YIQ and HSI. Then, the algorithm separately applies CLAHE in YIQ and HSI color spaces to obtain two different enhanced images. After that, the YIQ and HSI enhanced images are respectively converted back to RGB space. When the three components of red, green, and blue are not coherent in the YIQ-RGB or HSI-RGB images, the three components will have to be harmonized with the CLAHE algorithm in RGB space. Finally, using a 4-direction Sobel edge detector in the bounded general logarithm ratio operation, a self-adaptive weight selection nonlinear image enhancement is carried out to fuse the YIQ-RGB and HSI-RGB images together to achieve the final image. The experimental results showed that the proposed algorithm provided more detail enhancement and higher values of color restoration than other image enhancement algorithms. The proposed algorithm can effectively reduce noise interference and observably improve the image quality for underwater images.
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Kalaivani., N., Mozhi. N. Kani, M. Kanimozhi., S. Kalieswari., and R. Kuralarasi. "Endomicroscopy Image Recognition using Ensemble Neural network with Contrast Limited Adaptive Histogram Equalisation." International Journal of Engineering and Advanced Technology (IJEAT) 9, no. 4 (2020): 44–49. https://doi.org/10.35940/ijeat.C6438.049420.
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Endomicroscopy is a small tool used for cancer diagnosis, this enables in-vivo imaging at microscopic resolution closely to histology image during endoscopic procedures and captured image within the dataset has high imaging quality resulting in an inequality between moral and poor-quality images. There's no clear demonstration of the artifacts in an endomicroscopy producer. During this proposed method, the ensemble neural network (ENN) approach models to scale back the variance of predictions and reduce generalization error with contrast limited adaptive histogram equalization (CLAHE) algorithm were used to recover the image pixel balancing. Binary classification of accuracy 98.79% has been achieved.
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Risma, Vita Melati, and Ema Utami. "Tuberculosis Diagnosis From X-Ray Images Using Deep Learning And Contrast Enhancement Techniques." Jurnal Teknik Informatika (Jutif) 6, no. 2 (2025): 981–94. https://doi.org/10.52436/1.jutif.2025.6.2.4315.
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Tuberculosis (TB) is an infectious disease that poses a global health threat. Early diagnosis through chest X-ray (CXR) imaging is effective in reducing transmission and improving patient recovery rates. However, the limited number of radiologists in high TB burden areas hampers rapid and accurate detection. This study aims to improve TB diagnosis accuracy using deep learning models. Convolutional Neural Networks (CNN) are applied to analyze CXR images to support automated detection in regions with limited radiology personnel. The method involves image processing using Contrast Limited Adaptive Histogram Equalization (CLAHE) to enhance image quality. A public dataset consisting of 2,188 images was used, with preprocessing steps including resizing, normalization, and augmentation. The DenseNet201 model was employed as the main architecture, trained for 10 epochs with various batch sizes to evaluate its performance. Results show that the combination of CLAHE and DenseNet201 achieved the highest accuracy of 94.84%. Image quality enhancement with CLAHE proved to improve accuracy compared to models without preprocessing. This research contributes to enhancing the efficiency of automated early TB detection, reducing reliance on radiologists, and accelerating clinical decision-making.
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Farah, F. Alkhalid, Mudher Hasan Ahmed, and A. Alhamady Ahmed. "Improving radiographic image contrast using multi layers of histogram equalization technique." International Journal of Artificial Intelligence (IJ-AI) 10, no. 1 (2021): 151–56. https://doi.org/10.11591/ijai.v10.i1.pp151-156.
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Usually, X-ray image has distortion in many parts because it is focusing on bones rather than other, However, when dentist needs to make decision analysis, he does that by using X-ray and many opinions can be judged by looking closely on it like (inflammation, infection, tooth nerve, root of the tooth…). This paper proposes on new suggested technique by applying multilayers of histogram equalization (HE) and contrast limited adaptive histogram equalization (CLAHE) in order to make high contrast of X-ray, this technique provides very satisfied results and smooth intensity which leads to high clear X-ray image, by using Python3 and OpenCV.
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Miranda, Novelita Dwi, Ledya Novamizanti, and Syamsul Rizal. "CONVOLUTIONAL NEURAL NETWORK PADA KLASIFIKASI SIDIK JARI MENGGUNAKAN RESNET-50." Jurnal Teknik Informatika (Jutif) 1, no. 2 (2020): 61–68. http://dx.doi.org/10.20884/1.jutif.2020.1.2.18.
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Pengenalan sidik jari merupakan bagian dari teknologi biometrik. Klasifikasi sidik jari yang paling popular adalah Henry classification system. Henry membagi sidik jari berdasarkan garis polanya menjadi lima kelas yaitu arch (A), tented arch (T), left loop (L), right loop (R), dan whorl (W). Penelitian ini menggunakan Convolutional Neural Network (CNN) dengan model arsitektur Residual Network-50 (ResNet-50) untuk mengembangkan sistem klasifikasi sidik jari. Dataset yang digunakan diperoleh dari website National Institute of Standards and Technology (NIST) berupa citra sidik jari grayscale 8-bit. Hasil pengujian menunjukkan bahwa pemrosesan awal Contrast Limited Adaptive Histogram Equalization (CLAHE) dalam model CNN dapat meningkatkan performa akurasi dari sistem klasifikasi sidik jari sebesar 11,79%. Pada citra tanpa CLAHE diperoleh akurasi validasi 83,26%, sedangkan citra dengan CLAHE diperoleh akurasi validasi 95,05%.
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Saifullah, Shoffan, Andri Pranolo, and Rafał Dreżewski. "Comparative analysis of image enhancement techniques for braintumor segmentation: contrast, histogram, and hybrid approaches." E3S Web of Conferences 501 (2024): 01020. http://dx.doi.org/10.1051/e3sconf/202450101020.
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This study systematically investigates the impact of image enhancement techniques on Convolutional Neural Network (CNN)-based Brain Tumor Segmentation, focusing on Histogram Equalization (HE), Contrast Limited Adaptive Histogram Equalization (CLAHE), and their hybrid variations. Employing the U-Net architecture on a dataset of 3064 Brain MRI images, the research delves into preprocessing steps, including resizing and enhancement, to optimize segmentation accuracy. A detailed analysis of the CNN-based U-Net architecture, training, and validation processes is provided. The comparative analysis, utilizing metrics such as Accuracy, Loss, MSE, IoU, and DSC, reveals that the hybrid approach CLAHE-HE consistently outperforms others. Results highlight its superior accuracy (0.9982, 0.9939, 0.9936 for training, testing, and validation, respectively) and robust segmentation overlap, with Jaccard values of 0.9862, 0.9847, and 0.9864, and Dice values of 0.993, 0.9923, and 0.9932 for the same phases, emphasizing its potential in neuro-oncological applications. The study concludes with a call for refinement in segmentation methodologies to further enhance diagnostic precision and treatment planning in neuro-oncology.
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Kryjak, Tomasz, Krzysztof Blachut, Hubert Szolc, and Mateusz Wasala. "Real-Time CLAHE Algorithm Implementation in SoC FPGA Device for 4K UHD Video Stream." Electronics 11, no. 14 (2022): 2248. http://dx.doi.org/10.3390/electronics11142248.
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One of the problems encountered in the field of computer vision and video data analysis is the extraction of information from low-contrast images. This problem can be addressed in several ways, including the use of histogram equalisation algorithms. In this work, a method designed for this purpose—the Contrast-Limited Adaptive Histogram Equalization (CLAHE) algorithm—is implemented in hardware. An FPGA platform is used for this purpose due to the ability to run parallel computations and very low power consumption. To enable the processing of a 4K resolution (UHD, 3840 × 2160 pixels) video stream at 60 fps (frames per second) by using the CLAHE method, it is necessary to use a vector data format and process multiple pixels simultaneously. The algorithm realised in this work can be a component of a larger vision system, such as in autonomous vehicles or drones, but it can also support the analysis of underwater, thermal, or medical images both by humans and in an automated system.
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Alagulskhmi, A. "IMAGE RECOGNITION AND IDENTIFICATION USING MACHINE LEARNING." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, no. 12 (2023): 1–9. http://dx.doi.org/10.55041/ijsrem27729.
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Face recognition has been a rapidly growing and intriguing region progressively applications. A huge number of face recognition calculation have been produced in a long time ago. In this paper, for face detection we are using HOG (Histogram of oriented Gradient) based face detector which gives more accurate results rather than other machine learning algorithms like Haar Cascade. In recognition process we are using CLAHE (Contrast Limited Adaptive Histogram equalization) for pre-processing than we are using HOG which is a standard technique for features extraction. HOG features are extracted for the test image and also for the training images. And finally for classification we are using SVM (support vector machine). SVM will classify the HOG features. Pre-processing technique is use to remove the noise, contrast enhancement, and illumination equalization. The result of this paper show the liability and productiveness in better face recognition performance. Key Words: Face detection, Face recognition, Machine Learning, Support Vector Machine, CLAHE.
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Vinoothna, Boppudi. "Design and Development of Contrast-Limited Adaptive Histogram Equalization Technique for Enhancing MRI Images by Improving PSNR, UIQI Parameters in Comparison with Median Filtering." ECS Transactions 107, no. 1 (2022): 14819–27. http://dx.doi.org/10.1149/10701.14819ecst.
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Image enhancement is used to improve the quality of images and it enhances, sharpens image features, such as edges, boundaries, and contrast, to make a graphic display useful for display and analysis. In order to enhance the quality of MRI images, histogram-based image enhancement technique is developed in this work. Materials and Methods: In this research, a Contrast Limited Adaptive Histogram Equalization (CLAHE) based image enhancement technique is proposed and developed for MRI images and the proposed work is compared with another image enhancement technique called Median Filtering (MF) method. Input medical images (N=30) of both group were downloaded from standard medical database. The enrollment ratio is obtained as 1 with 95% confidence interval and a threshold value 0.05. Results: The performance of image enhancement is measured using two parameters namely, Peak Signal Noise Ratio (PSNR) and Universal Image Quality Index (UIQI). These parameters are calculated and evaluated to assess the proposed methods efficacy. High values of PSNR and UIQI indicate better enhancement. CLAHE provides mean PSNR values of 18.6968(dB), mean UIQI of 80.9220%, and median filtering method provides mean PSNR values of 14.2261(dB) and mean UIQI of 76.3463%. Conclusion: Based on the experiment's results, the CLAHE image enhancement technique significantly performed better than the MF technique.
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Gajula, Srinivasarao, and V. Rajesh. "MRI Brain Image Segmentation by Fully Convectional U-Net." Revista Gestão Inovação e Tecnologias 11, no. 1 (2021): 6035–42. http://dx.doi.org/10.47059/revistageintec.v11i1.1877.
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When there is rapid growth in the research, and it will lead to use off large amount of data to get accurate results. When you are having large number of data then we require new techniques that will gives better performance in processing. The segmentation of a brain tumour is critical for both treatment and prevention. Various researchers proposed different neural network architectures to get better performance in segmentation of the brain tumour. processing this huge data is challenging and time taking process for computational and analysis. In this paper we are discussing about image segmentation by using fully conventional network U-Net. In the first stage we are performing some pre-processing on data sets by using adaptive filters. In the next step we are using U-Net architecture to perform segmentation and prediction of MRI brain images. In the next step we are performing Contrast Limited Adaptive Histogram Equalization (CLAHE) to equalize images. CLAHE takes care of over-amplification of the contrast. CLAHE operates on tiles of the image, rather than entire image.
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Raharjo, Ahmad Solikhin Gayuh, and Endang Sugiharti. "Alphabet Classification of Sign System Using Convolutional Neural Network with Contrast Limited Adaptive Histogram Equalization and Canny Edge Detection." Scientific Journal of Informatics 10, no. 3 (2023): 239–50. http://dx.doi.org/10.15294/sji.v10i3.44137.
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Purpose: There are deaf people who have problems in communicating orally because they do not have the ability to speak and hear. The sign system is used as a solution to this problem, but not everyone understands the use and meaning of the sign system, even in terms of the alphabet. Therefore, it is necessary to classify a sign system in the form of American Sign Language (ASL) using Artificial Intelligence technology to get good results.Methods: This research focuses on improving the accuracy of ASL alphabet classification using the VGG-19 and ResNet50 architecture of the Convolutional Neural Network (CNN) method combined with Contrast Limited Adaptive Histogram Equalization (CLAHE) to improve the detail quality of images and Canny Edge Detection to produce images that focus on the objects in it. The focused result is the accuracy value. This study uses the ASL alphabet dataset from Kaggle.Result: Based on the test results, there are three best accuracy results. The first is using the ResNet50 architecture, CLAHE, and an image size of 128 x 128 pixels with an accuracy of 99.9%, followed by the ResNet50 architecture, CLAHE + Canny Edge Detection, and an image size of 128 x 128 pixels with an accuracy of 99.82 %, and in third place are the VGG-19 architecture, CLAHE, and an image size of 128 x 128 pixels with an accuracy of 98.93%.Novelty: The novelty of this study is the increase in the accuracy value of ASL image classification from previous studies.
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Udayana, I. Putu Agus Eka Darma, I. Made Karang Satria Prawira, and I. Gede Bagus Arya Merta Tika. "Comparison of Artificial Intelligence Methods for Tuberculosis Detection Using X-Ray Images." IJCCS (Indonesian Journal of Computing and Cybernetics Systems) 19, no. 1 (2025): 49. https://doi.org/10.22146/ijccs.102601.
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Penyakit tuberkulosis (TB), yang disebabkan oleh bakteri Mycobacterium tuberculosis, merupakan penyakit menular yang sangat berbahaya. Di Indonesia, TB adalah penyakit menular paling mematikan setelah COVID-19 dan menempati urutan ke-13 sebagai penyebab kematian global. Deteksi dini TB sangat penting untuk meningkatkan peluang kesembuhan, namun keterbatasan jumlah ahli radiologi menjadi tantangan utama. Teknologi deep learning, khususnya Convolutional Neural Network (CNN), mejadi solusi efektif untuk masalah ini. Oleh karena itu, pada penelitian ini akan membandingkan dua arsitektur CNN, yaitu AlexNet dan VGG-19, dalam mendeteksi TB pada citra rontgen paru-paru, dengan penerapan metode perbaikan kualitas citra, seperti Histogram Equalization (HE), Adaptive Histogram Equalization (AHE), Contrast Limited Adaptive Histogram Equalization (CLAHE), dan Gamma Correction. Dataset yang digunakan diperoleh dari Kaggle dan mencakup citra rontgen paru-paru normal serta TB. Evaluasi performa dilakukan berdasarkan akurasi, presisi, recall, dan F1-score. Hasil penelitian menunjukkan bahwa VGG-19 dengan CLAHE memberikan performa terbaik dengan akurasi 93.5%, presisi 98.88%, recall 88%, dan F1-score 93.12%. VGG-19 dengan Gamma Correction juga menunjukkan hasil yang sangat baik dengan akurasi 91%, presisi 97.67%, recall 84%, dan F1-score 90.32%. Temuan ini menggarisbawahi efektivitas kombinasi CNN dan metode pemrosesan citra dalam meningkatkan deteksi TB.
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Nikhil Raje. "Hybrid DL Models for Improved Accuracy in Diagnosing Chronic Obstructive Pulmonary Disease." Advances in Nonlinear Variational Inequalities 27, no. 4 (2024): 385–91. http://dx.doi.org/10.52783/anvi.v27.1605.
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Chronic Obstructive Pulmonary Disease (COPD) is a common respiratory disorder marked by enduring airflow obstruction, leading to considerable illness and death rates. Timely and precise diagnosis is essential for proper management and treatment. In this study, we present a novel hybrid deep learning (DL) model leveraging an Autoencoder-GAN (Generative Adversarial Network) architecture to improve the accuracy of COPD diagnosis. Our approach incorporates a cutting-edge preprocessing method, Adaptive Histogram Equalization with Contrast Limited Adaptive Histogram Equalization (CLAHE), to enhance the contrast and detail in CXR images, facilitating more precise feature extraction. The proposed Autoencoder-GAN Hybrid Model significantly outperforms traditional models, achieving an impressive accuracy of 98.3%. By enhancing image quality and focusing on key features through CLAHE preprocessing, our model is able to better distinguish between healthy and COPD-affected lungs. We compared the performance of our model with standard DL models, including CNN, SVM and Random Forest Classifiers, demonstrating superior results across various evaluation metrics. This study highlights the potential of advanced DL techniques and innovative preprocessing methods to enhance the accuracy of COPD diagnosis, offering a promising tool for healthcare professionals in the early detection and management of this chronic disease.
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Sarif, Akhmad, and Dadang Gunawan. "Perbandingan Metode Penyesuaian Kontras Citra Pada Pengenalan Ekspresi Wajah Menggunakan Fine-Tuning AlexNet." JURNAL MEDIA INFORMATIKA BUDIDARMA 7, no. 3 (2023): 1144. http://dx.doi.org/10.30865/mib.v7i3.6382.
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Research related to facial expression recognition (FER) has become a significant topic of interest in the field of computer vision due to its broad applications. Artificial intelligence technologies, such as deep learning, have been applied in FER research. The use of deep learning models in FER requires a dataset for training, which plays a crucial role in determining the performance of deep learning. However, the available FER datasets often require preprocessing before being processed using deep learning. In this study, a comparison of contrast adjustment preprocessing methods was conducted using Histogram Equalization (HE) and Contrast Limited Adaptive Histogram Equalization (CLAHE). Subsequently, the dataset images were used with a fine-tuned deep learning model, specifically AlexNet, to classify them according to the categories of human facial expressions. The objective of this research is to determine the superior contrast adjustment method for FER dataset images in improving the performance of the deep learning model employed. The CK+ dataset (The Extended Cohn-Kanade) and KDEF dataset (The Karolinska Directed Emotional Faces) were used in this study. The results indicate that the CLAHE method outperforms HE in both the CK+ and KDEF datasets. In the CK+ dataset, the CLAHE method achieved an average accuracy of 93.21%, while the average accuracy of the HE method was 91.50%. For the KDEF dataset, the average accuracy of the CLAHE method was 88.35%, compared to 84.70% for the HE method.
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Khozaimi, Ach, Isnani Darti, Syaiful Anam, and Wuryansari Muharini Kusumawinahyu. "Advanced cervical cancer classification: enhancing pap smear images with hybrid PMD filter-CLAHE." Indonesian Journal of Electrical Engineering and Computer Science 39, no. 1 (2025): 644. https://doi.org/10.11591/ijeecs.v39.i1.pp644-655.
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Cervical cancer remains a significant health problem, especially in developing countries. Early detection is critical for effective treatment. Convolutional neural networks (CNN) have shown promise in automated cervical cancer screening, but their performance depends on pap smear image quality. This study investigates the impact of various image preprocessing techniques on CNN performance for cervical cancer classification using the SIPaKMeD dataset. Three preprocessing techniques were evaluated: PeronaMalik diffusion (PMD) filter for noise reduction, contrast-limited adaptive histogram equalization (CLAHE) for image contrast enhancement, and the proposed hybrid PMD filter-CLAHE approach. The enhanced image datasets were evaluated on pretrained models, such as ResNet-34, ResNet-50, SqueezeNet-1.0, MobileNet-V2, EfficientNet-B0, EfficientNet-B1, DenseNet121, and DenseNet-201. The results show that hybrid preprocessing PMD filter-CLAHE can improve the pap smear image quality and CNN architecture performance compared to the original images. The maximum metric improvements are 13.62% for accuracy, 10.04% for precision, 13.08% for recall, and 14.34% for F1-score. The proposed hybrid PMD filter-CLAHE technique offers a new perspective in improving cervical cancer classification performance using CNN architectures.
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Lu, Peng, and Qingjiu Huang. "Robotic Weld Image Enhancement Based on Improved Bilateral Filtering and CLAHE Algorithm." Electronics 11, no. 21 (2022): 3629. http://dx.doi.org/10.3390/electronics11213629.
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Robotic welding requires a higher weld image resolution for easy weld identification; however, the higher the resolution, the higher the cost. Therefore, this paper proposes an improved CLAHE algorithm, which can not only effectively denoise and retain edge information but also improve the contrast of images. First, an improved bilateral filtering algorithm is used to process high-resolution images to remove noise while preserving edge details. Then, the CLAHE (Contrast Limited Adaptive Histogram Equalization) algorithm and Gaussian masking algorithm are used to enhance the enhanced image, and then differential processing is used to reduce the noise in the two images, while preserving the details of the image, enhancing the image contrast, and obtaining the final enhanced image. Finally, the effectiveness of the algorithm is verified by comparing the peak signal-to-noise ratio and structural similarity with other algorithms.
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Archana B and K. Kalirajan. "Contrast Enhancement of Alzheimer’s MRI using Histogram Analysis." Journal of Innovative Image Processing 5, no. 4 (2023): 379–89. http://dx.doi.org/10.36548/jiip.2023.4.003.
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Contrast enhancement of MRI images frequently needs considerable pre-processing to provide accurate data for disease diagnosis and proper treatment. Enhancing the appearance of medical images becomes a difficult task owing to the uncertainty of the obtained image quality. In this study, Alzheimer’s MRI images are subjected to a contrast enhancement algorithm for easy diagnosis. A noise reduction and contrast enhancement technique for MRI images is discussed in this research. Histogram-based algorithms are used to solve the problems of de-noising and enhancing the contrast of images for identification of the infected region. The proposed method is based on contrast-limited adaptive histogram equalization (CLAHE) and the comparison with Histogram Equalization (HE). The suggested enhancement technique's performance can be evaluated using several metrics, including Structure Similarity Index Measure (SSIM), and Peak Signal-to-Noise Ratio (PSNR). Observational studies revealed that the suggested approach is significantly more efficient than the basic enhancement techniques such as HE.
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Mapayi, Temitope, Serestina Viriri, and Jules-Raymond Tapamo. "Comparative Study of Retinal Vessel Segmentation Based on Global Thresholding Techniques." Computational and Mathematical Methods in Medicine 2015 (2015): 1–15. http://dx.doi.org/10.1155/2015/895267.
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Due to noise from uneven contrast and illumination during acquisition process of retinal fundus images, the use of efficient preprocessing techniques is highly desirable to produce good retinal vessel segmentation results. This paper develops and compares the performance of different vessel segmentation techniques based on global thresholding using phase congruency and contrast limited adaptive histogram equalization (CLAHE) for the preprocessing of the retinal images. The results obtained show that the combination of preprocessing technique, global thresholding, and postprocessing techniques must be carefully chosen to achieve a good segmentation performance.
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F. Alkhalid, Farah, Ahmed Mudher Hasan, and Ahmed A. Alhamady. "Improving radiographic image contrast using multi layers of histogram equalization technique." IAES International Journal of Artificial Intelligence (IJ-AI) 10, no. 1 (2021): 151. http://dx.doi.org/10.11591/ijai.v10.i1.pp151-156.
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<span id="docs-internal-guid-43432eef-7fff-9949-6deb-865191ff0740"><span>Usually, X-ray image has distortion in many parts because it is focusing on bones rather than other, However, when dentist needs to make decision analysis, he does that by using X-ray and many opinions can be judged by looking closely on it like (inflammation, infection, tooth nerve, root of the tooth…). This paper proposes on new suggested technique by applying multilayers of histogram equalization (HE) and contrast limited adaptive histogram equalization (CLAHE) in order to make high contrast of X-ray, this technique provides very satisfied results and smooth intensity which leads to high clear X-ray image, by using Python3 and OpenCV.</span></span>
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Hana, F. M., and I. D. Maulida. "Analysis of contrast limited adaptive histogram equalization (CLAHE) parameters on finger knuckle print identification." Journal of Physics: Conference Series 1764, no. 1 (2021): 012049. http://dx.doi.org/10.1088/1742-6596/1764/1/012049.
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Reza, Ali M. "Realization of the Contrast Limited Adaptive Histogram Equalization (CLAHE) for Real-Time Image Enhancement." Journal of VLSI Signal Processing-Systems for Signal, Image, and Video Technology 38, no. 1 (2004): 35–44. http://dx.doi.org/10.1023/b:vlsi.0000028532.53893.82.
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Attia, Salim J. "Assessment of Some Enhancement Methods of Renal X-ray Image." NeuroQuantology 18, no. 12 (2020): 01–05. http://dx.doi.org/10.14704/nq.2020.18.12.nq20231.
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The study focuses on assessment of the quality of some image enhancement methods which were implemented on renal X-ray images. The enhancement methods included Imadjust, Histogram Equalization (HE) and Contrast Limited Adaptive Histogram Equalization (CLAHE). The images qualities were calculated to compare input images with output images from these three enhancement techniques. An eight renal x-ray images are collected to perform these methods. Generally, the x-ray images are lack of contrast and low in radiation dosage. This lack of image quality can be amended by enhancement process. Three quality image factors were done to assess the resulted images involved (Naturalness Image Quality Evaluator (NIQE), Perception based Image Quality Evaluator (PIQE) and Blind References Image Spatial Quality Evaluator (BRISQE)). The quality of images had been heightened by these methods to support the goals of diagnosis. The results of the chosen enhancement methods of collecting images reflected more qualified images than the original images. According to the results of the quality factors and the assessment of radiology experts, the CLAHE method was the best enhancement method.
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Phimphisan, Songgrod, and Nattavut Sriwiboon. "A Customized CNN Architecture with CLAHE for Multi-Stage Diabetic Retinopathy Classification." Engineering, Technology & Applied Science Research 14, no. 6 (2024): 18258–63. https://doi.org/10.48084/etasr.8932.
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This paper presents a customized Convolutional Neural Network (CNN) architecture for multi-stage detection of Diabetic Retinopathy (DR), a leading cause of vision impairment and blindness. The proposed model incorporates advanced image enhancement techniques, particularly Contrast Limited Adaptive Histogram Equalization (CLAHE), to improve the visibility of critical retinal features associated with DR. By integrating CLAHE with a finely tuned CNN, the proposed approach significantly enhances accuracy and robustness, allowing for more precise detection across various stages of DR. The proposed model was evaluated against several state-of-the-art techniques, with the customized CNN alone achieving an overall accuracy of 97.69%. The addition of CLAHE further boosts the performance, achieving an accuracy of 99.69%, underscoring the effectiveness of combining CLAHE with CNN for automated DR detection. This approach provides an efficient, scalable, and highly accurate solution for early and multistage DR detection, which is crucial for timely intervention and prevention of vision loss.
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Dissopa, Jessada, Supaporn Kansomkeat, and Sathit Intajag. "Enhance Contrast and Balance Color of Retinal Image." Symmetry 13, no. 11 (2021): 2089. http://dx.doi.org/10.3390/sym13112089.
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This paper proposes a simple and effective retinal fundus image simulation modeling to enhance contrast and adjust the color balance for symmetric information in biomedicine. The aim of the study is for reliable diagnosis of AMD (age-related macular degeneration) screening. The method consists of a few simple steps. Firstly, local image contrast is refined with the CLAHE (Contrast Limited Adaptive Histogram Equalization) technique by operating CIE L*a*b* color space. Then, the contrast-enhanced image is stretched and rescaled by a histogram scaling equation to adjust the overall brightness offsets of the image and standardize it to Hubbard’s retinal image brightness range. The proposed method was assessed with retinal images from the DiaretDB0 and STARE datasets. The findings in the experimentation section indicate that the proposed method results in delightful color naturalness along with a standard color of retinal lesions.
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Hamdani, Ibnu Mansyur, Ismi Rizqa Lina, and Muhammad Takdir Muslihi. "Deteksi Tepi Optimal dengan Integrasi Canny, CLAHE, dan Perona-Malik Diffusion Filter." Jurnal Mosfet 5, no. 1 (2025): 127–36. https://doi.org/10.31850/jmosfet.v5i1.3638.
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Edge detection is a fundamental technique in digital image processing, crucial for identifying object boundaries. However, detecting edges in low-intensity and noisy images remains a significant challenge. This study proposes an optimal edge detection method by integrating the Canny algorithm, Contrast Limited Adaptive Histogram Equalization (CLAHE), and Perona-Malik Diffusion Filter, with automatic kappa (k) value determination using the Fractional Order Sobel Mask. The process begins with noise reduction through the Perona-Malik Diffusion Filter, followed by local contrast enhancement using CLAHE, and concludes with edge detection via the Canny algorithm. Experimental results demonstrate that the proposed method significantly enhances edge clarity and robustness against noise compared to the conventional Canny algorithm, particularly for low-intensity images and images with noise. Tests on leaf and medical images confirm the effectiveness of this method in improving edge detection quality in digital images.