Relevant bibliographies by topics / Contrast Limited Adaptive Histogram Equivalent (CLAHE)

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Academic literature on the topic 'Contrast Limited Adaptive Histogram Equivalent (CLAHE)'

Author: Grafiati
Published: 4 June 2025
Last updated: 23 June 2025

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Journal articles on the topic "Contrast Limited Adaptive Histogram Equivalent (CLAHE)"

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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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Book chapters on the topic "Contrast Limited Adaptive Histogram Equivalent (CLAHE)"

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Devi, D. Anjani Suputri, D. Sasi Rekha, Mudugu Kishore Kumar, P. Rama Mohana Rao, and G. Naga Vallika. "Transfer Learning-Based Effective Facial Emotion Recognition Using Contrast Limited Adaptive Histogram Equalization (CLAHE)." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-6690-5_20.

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Surya, S., and A. Muthukumaravel. "Adaptive Sailfish Optimization-Contrast Limited Adaptive Histogram Equalization (ASFO-CLAHE) for Hyperparameter Tuning in Image Enhancement." In Computational Intelligence for Clinical Diagnosis. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-23683-9_5.

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Saravanan, P., and K. Vadivazhagan. "Underwater Image Classification with Sloth-Enhanced Contrast Limited Adaptive Histogram Equalization (S-CLAHE) and Deep Learning Algorithms." In Communications in Computer and Information Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-86296-0_3.

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Kurt Burçin, Nabiyev Vasif V., and Turhan Kemal. "Comparison of Enhancement Methods for Mammograms with Performance Measures." In Studies in Health Technology and Informatics. IOS Press, 2014. https://doi.org/10.3233/978-1-61499-432-9-486.

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Mammograms are generally contaminated by noise which assures the need for image enhancement to aid interpretation. The enhancement of mammograms is a very important problem for easy extraction of suspicious regions known as regions of interest (ROIs). This paper introduces comparison of various hybrid enhancement algorithms based on mathematical morphology, contrast stretching, wavelet transform, anisotropic diffusion filter and contrast limited adaptive histogram equalization (CLAHE). The performances of algorithms have been compared by using three global image enhancement evaluation measures; Enhancement Measure (EME), Absolute Mean Brightness Error (AMBE) and Peak Signal-to-Noise Ratio (PSNR). For this study, we have used MIAS database. Experimental results show that the combination of mathematical morphology, anisotropic diffusion filter and CLAHE methods, yields significantly superior image quality and provides more visibility for the suspicious regions.
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Sood, Neetu, Indu Saini, Tarannum Awasthi, Milin Kaur Saini, Parul Bhoriwal, and Tanveer Kaur. "Fog Removal Algorithms for Real-Time Video Footage in Smart Cities for Safe Driving." In Practice, Progress, and Proficiency in Sustainability. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-8085-0.ch003.

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In this chapter, different approaches are presented for removal of fog from video footage taken in moving cars. The methodology uses different approaches, namely dark channel prior, contrast limited adaptive histogram equalization (CLAHE), the combination of two approaches (dark channel prior and CLAHE), and RETINEX algorithm combined with DWT. The algorithms are implemented in MATLAB R2015a. Moreover, the algorithms are compared based on their computational complexity and a visibility metric which is used for computing the CNR of video frames before and after the application of the algorithm. The chapter discusses which algorithm would provide better performance during night fog and daylight fog. Finally, the safe speed of the driver is calculated based on the time complexity of the algorithm used.
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Bhimavarapu, Usharani. "Fashion Product Recommendation Systems." In Sustainable Futures With Life Cycle Assessment in Industry 5.0. IGI Global, 2025. https://doi.org/10.4018/979-8-3693-9346-8.ch012.

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A comprehensive fashion product image dataset comprising 40,000 images was curated, encompassing six major categories such as apparel, accessories, footwear, personal care, and sporting goods, along with 44 subcategories like top wear, shoes, bags, and belts. Preprocessing techniques, including Gaussian Laplace distribution and Contrast Limited Adaptive Histogram Equalization (CLAHE), were employed to enhance image quality by emphasizing key features and improving contrast. Feature extraction was conducted using Particle Swarm Optimization (PSO), which efficiently reduced dimensionality by selecting the most relevant features while preserving critical information. A recommender system was then developed using the Fuzzy C-Means (FCM) clustering approach, enabling personalized and accurate recommendations by leveraging overlapping feature clusters. This integrated framework offers a robust foundation for analyzing and recommending fashion products, addressing the complexities of the fashion domain
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Escorcia-Gutierrez José, Torrents-Barrena Jordina, Romero-Aroca Pedro, Valls Aida, and Puig Domènec. "Interactive Optic Disk Segmentation via Discrete Convexity Shape Knowledge Using High-Order Functionals." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2016. https://doi.org/10.3233/978-1-61499-696-5-39.

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Diabetic Retinopathy (DR) has become nowadays a considerable world-wide threat due to increased growth of blind people at early ages. From the engineering viewpoint, the detection of DR pathologies (microaneurysms, hemorrhages and exudates) through computer vision techniques is of prime importance in medical assistance. Such methodologies outperform traditional screening of retinal color fundus images. Moreover, the identification of landmark features as the optic disk (OD), fovea and retinal vessels is a key pre-processing step to detect the aforementioned potential pathologies. In the same vein, this paper works with the well-known Convexity Shape Prior algorithm to segment the main anatomical structure of the retina, the OD. At first, some pre-processing techniques such as the Contrast Limited Adaptive Histogram Equalization (CLAHE) and Brightness Preserving Dynamic Fuzzy Histogram Equalization (BPDFHE) are applied to enhance the image contrast and eliminate the artifacts. Subsequently, several morphological operations are performed to improve the post-segmentation of the OD. Finally, blood vessels are extracted through a novel fusion of the average, median, Gaussian and Gabor wavelet filters.
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Garcia Rios, Elizabeth, Enrique Escamilla Hernandez, Angela Gabriela Espino Lopez, Héctor Manuel Perez Meana, and Lorena Mendoza Guzman. "Detection and Identification of Respiratory Disease Using DWT and SVM." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2024. http://dx.doi.org/10.3233/faia240394.

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This article proposes a computer-aided diagnosis (CAD) system for the detection and identification of respiratory diseases, such as COVID-19, pneumonia, etc., in addition to differentiating a healthy case. Starting then, the proposal is comprised of the Discrete Wavelet Transform (DWT) with the Symlet10 function for the extraction of main features, together with the Limited Contrast Adaptive Histogram (CLAHE) method for contrast enhancement. For the classification of the cases, the Support Vector Machine (SVM) was used. The results showed considerable performance with the Medium Gaussian SVM model delivering 82.4% of correctly estimated values. Improve the capacity of detection and identification based on a supervised learning algorithm without the need to use high computational performance, considering that, in most of the health systems in Mexico, there is not the necessary hardware for the installation and operation of systems with high computational demand requirements.
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Subramani, Sangeetha, and N. Suganthi. "Kho Yolo Net." In Advances in Marketing, Customer Relationship Management, and E-Services. IGI Global, 2025. https://doi.org/10.4018/979-8-3693-6468-0.ch012.

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India's population largely depends on agriculture for their livelihood, making agricultural research vital for the nation's economy. Plant leaf disease diagnosis is essential for ensuring healthy crop production and minimizing financial losses, as plant diseases can significantly reduce crop yield and quality. Machine learning (ML), particularly image-based analysis, has revolutionized plant disease detection. To address plant leaf disease identification via image analysis, a novel KHO-YOLO Net technique has been developed. Contrast Limited Adaptive Histogram Equalization (CLAHE) is applied to plant leaf images to improve their quality. YOLO v8 is used to detect multiple diseases within a single image. YOLOv8 is optimized using the Krill Herd Optimization (KHO) technique for superior classification outcomes. The efficacy of the proposed system is compared to YOLOv3, YOLOv4, YOLOv5, and YOLOv6, with the proposed YOLOv8 demonstrating an overall accuracy improvement to 99.59%.
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Kaur, Rajwinder, Richa Brar, and Gagandeep Jagdev. "Comparative Analysis and Implementation of CLAHE with Proposed Technique in Retinal Images." In New Frontiers in Communication and Intelligent Systems. Soft Computing Research Society, 2021. http://dx.doi.org/10.52458/978-81-95502-00-4-9.

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The human eye is the most critical part that can be adversely affected by diabetes in the near or long run. The detection of DR (Diabetic Retinopathy) in the early stages is not that easily possible. The detection of DR primarily depends on the quality of the retinal images of the diseased person. The higher is the quality of the retinal image, the more are the chances of detection of abnormalities. The research paper elaborates on the popular CLAHE (Contrast Limited Adaptive Histogram Equalization) technique been used for enhancing the contrast of the retinal images. The research paper also anticipates a technique intended for enhancing the quality of the retinal images. The retinal images are obtained from the DRIVE (Digital Retinal Images for Vessel Extraction) dataset. The quality of enhancement achieved by using both techniques is compared and measured using three performance metrics of MSE (Mean Square Error), PSNR (Peak Signal Noise Ratio), and RMSE (Root Mean Square Error). PSNR is considered as the prominent performance metric to decide the degree of enhancement achieved. Greater is the value of the PSNR, higher is the degree of enhancement. The comparative analyses between the PSNR values obtained from both the techniques have been conducted and it has been found that the proposed technique outperforms CLAHE in enhancing the retinal images.
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Conference papers on the topic "Contrast Limited Adaptive Histogram Equivalent (CLAHE)"

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Alpan, Kezban, Bardia Arman, and Kamil Dimililer. "Effect of Contrast Limited Adaptive Histogram Equalization (CLAHE) on Breast Cancer Detection Using Residual Network (ResNet)." In 2025 1st International Conference on Computational Intelligence Approaches and Applications (ICCIAA). IEEE, 2025. https://doi.org/10.1109/icciaa65327.2025.11013776.

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Ferguson, Phillip David, Tughrul Arslan, Ahmet T. Erdogan, and Andrew Parmley. "Evaluation of contrast limited adaptive histogram equalization (CLAHE) enhancement on a FPGA." In 2008 IEEE International SOC Conference (SOCC). IEEE, 2008. http://dx.doi.org/10.1109/socc.2008.4641492.

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Zheng, Ruizhi, Qing Guo, Chang Gao, and Ming-an Yu. "A Hybrid Contrast Limited Adaptive Histogram Equalization (CLAHE) for Parathyroid Ultrasonic Image Enhancement." In 2019 Chinese Control Conference (CCC). IEEE, 2019. http://dx.doi.org/10.23919/chicc.2019.8866479.

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Kharel, Nabin, Abeer Alsadoon, P. W. C. Prasad, and A. Elchouemi. "Early diagnosis of breast cancer using contrast limited adaptive histogram equalization (CLAHE) and Morphology methods." In 2017 8th International Conference on Information and Communication Systems (ICICS). IEEE, 2017. http://dx.doi.org/10.1109/iacs.2017.7921957.

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Hendrawan, Aria, and Siti Asmiatun. "Identification of Picnosis Cells Using Contrast-Limited Adaptive Histogram Equalization (CLAHE) and K-means Algorithm." In 2018 1st International Conference on Computer Applications & Information Security (ICCAIS). IEEE, 2018. http://dx.doi.org/10.1109/cais.2018.8441978.

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Musa, Purnawarman, Farid Al Rafi, and Missa Lamsani. "A Review: Contrast-Limited Adaptive Histogram Equalization (CLAHE) methods to help the application of face recognition." In 2018 Third International Conference on Informatics and Computing (ICIC). IEEE, 2018. http://dx.doi.org/10.1109/iac.2018.8780492.

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Bhat, Madhukar, and Tarun Patil M S. "Adaptive clip limit for contrast limited adaptive histogram equalization (CLAHE) of medical images using least mean square algorithm." In 2014 International Conference on Advanced Communication, Control and Computing Technologies (ICACCCT). IEEE, 2014. http://dx.doi.org/10.1109/icaccct.2014.7019300.

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Khan, R., M. Talha, A. S. Khattak, and M. Qasim. "Realization of Balanced Contrast Limited Adaptive Histogram Equalization (B-CLAHE) for Adaptive Dynamic Range Compression of real time medical images." In 2013 10th International Bhurban Conference on Applied Sciences and Technology (IBCAST 2013). IEEE, 2013. http://dx.doi.org/10.1109/ibcast.2013.6512142.

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Salms, Andrejs, and Uldis Zaimis. "Investigation of impact of color saturation and hue on neural network-based object recognition in underwater environments." In 24th International Scientific Conference Engineering for Rural Development. Latvia University of Life Sciences and Technologies, Faculty of Engineering and Information Technologies, 2025. https://doi.org/10.22616/erdev.2025.24.tf071.

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Object detection in underwater environments is often impaired by low visibility and reduced contrast caused by turbidity and light absorption. This study investigates the impact of contrast enhancement techniques – specifically grayscale conversion and the application of Contrast Limited Adaptive Histogram Equalization (CLAHE) – on object recognition accuracy using deep learning models such as YOLOv8. Through the experimental analysis with YOLOv8, we compare the detection performance across RGB, grayscale and CLAHE-enhanced images, in scenario involving submerged automotive tires and nearly transparent objects such as a cellophane bag. The result demonstrates that CLAHE processing significantly improves detection confidence for opaque objects, increasing the average confidence score by 8%. However, it reduces the performance for translucent objects, with detection counts dropping by 44% and confidence scores reduced by 13%. These findings highlight the object-dependent nature of preprocessing methods and suggest that CLAHE is more beneficial in low-contrast scenarios involving opaque objects. This study underlines the importance of tailoring image enhancement strategies depending on object characteristics to optimize neural network-based object recognition in underwater conditions.
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Reis, Marcus Vinicius Diniz dos, and Pedro Moises de Sousa. "Quality Assessment of Coffee Beans Using Convolutional Neural Networks with Wavelet and CLAHE Techniques." In Workshop de Sistemas de Informação. Sociedade Brasileira de Computação, 2024. https://doi.org/10.5753/wsis.2024.33669.

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Abstract:
This paper presents an analytical study comparing different filtering techniques applied to a Convolutional Neural Network (CNN) for coffee bean classification. The results demonstrated that the CLAHE (Contrast Limited Adaptive Histogram Equalization) filter achieved the highest performance, with an accuracy of 0.8875 on the test set. The findings indicate that applying filtering techniques can enhance the performance of the ResNet18 network. CLAHE’s effectiveness is attributed to its ability to improve image details and contrast, leading to superior classification results. This study underscores the potential of advanced filtering methods to boost CNN performance in image classification tasks.
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