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Journal articles on the topic 'Histogram equalization'

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Published: 4 June 2021
Last updated: 7 July 2024

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1

Dhal, Krishna Gopal, Sankhadip Sen, Kaustav Sarkar, and Sanjoy Das. "Entropy based Range Optimized Brightness Preserved Histogram-Equalization for Image Contrast Enhancement." International Journal of Computer Vision and Image Processing 6, no. 1 (January 2016): 59–72. http://dx.doi.org/10.4018/ijcvip.2016010105.

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In this study the over-enhancement problem of traditional Histogram-Equalization (HE) has been removed to some extent by a variant of HE called Range Optimized Entropy based Bi-Histogram Equalization (ROEBHE). In ROEBHE image histogram has been thresholded into two sub-histograms i.e. histograms corresponding to background and foreground. The threshold is calculated by maximizing the sum of the entropy of these two sub-histograms. The range for equalization has been optimized by maximizing the Peak-Signal to Noise ratio (PSNR). The experimental results prove that ROEBHE has prevailed over existing methods and PSNR is a better range optimizer than Absolute Mean Brightness Error (AMBE).
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Yousuf, M. A., and M. R. H. Rakib. "An Effective Image Contrast Enhancement Method Using Global Histogram Equalization." Journal of Scientific Research 3, no. 1 (December 19, 2010): 43. http://dx.doi.org/10.3329/jsr.v3i1.5299.

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Image enhancement is one of the most important issues in low-level image processing. Histograms are the basis for numerous spatial domain processing techniques. In this paper, we present a simple and effective method for image contrast enhancement based on global histogram equalization. In this method, at first input image is normalized by making the minimum gray level value to 0. Then the probability of each grey level is calculated from the available ROI grey levels. Finally, histogram equalization is performed on the input image based on the calculated probability density (or distribution) function. As a result, the mean brightness of the input image does not change significantly by the histogram equalization. Additionally, noise is prevented from being greatly amplified. Experimental results on medical images demonstrate that the proposed method can enhance the images effectively. The result is also compared with the result of image enhancement technique using local statistics.Keywords: Histogram equalization; Global histogram equalization; Image enhancement; Local statistics.© 2011 JSR Publications. ISSN: 2070-0237 (Print); 2070-0245 (Online). All rights reserved.doi:10.3329/jsr.v3i1.5299 J. Sci. Res. 3 (1), 43-50 (2011)
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Stoel, B. C., A. M. Vossepoel, F. P. Ottes, P. L. Hofland, H. M. Kroon, and L. J. Schultze Kool. "Interactive histogram equalization." Pattern Recognition Letters 11, no. 4 (April 1990): 247–54. http://dx.doi.org/10.1016/0167-8655(90)90063-8.

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Husain, Nursuci Putri, and Nurseno Bayu Aji. "Pemanfaatan Histogram Equalization pada Local Tri Directional Pattern untuk Sistem Temu Kembali Citra." SPECTA Journal of Technology 4, no. 1 (April 1, 2020): 49–58. http://dx.doi.org/10.35718/specta.v4i1.164.

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Abstract Local tri-directional pattern (LtriDP) is a method of extracting local intensity features from each pixel based on direction. However, this method has not been able to provide good performance in extracting features for image retrieval. One reason that makes image retrieval performance worse is the effect of lighting. Lighting can cause large variations between images. This study proposed utilization of Histogram Equalization (HE). Histogram equalization is a functional method of stretching gray degrees and expanding image contrast. This will make variations in the gray level of the original image can be controlled. There are several main stages in this study, firstly query image and image dataset will be preprocessed with histogram equalization. After that, the image is extracted by a tri-directional pattern and magnitude pattern are searched. A tri-directional pattern will produce two histograms, while a magnitude pattern produces one histogram. The three histograms are combined or joint histogram is performed. Histogram that has been joint is a feature vector. The feature vector will be calculated using a similarity measurement Canberra. After that, an image similar to the query image will be obtained. The experiment was conducted using 3 face datasets namely ORL, BERN, and YALE. The average recall value was 0.422 for the ORL dataset, 0.50 for the BERN dataset, and 0.63 for the YALE dataset. The evaluation show, the proposed method can be used as a process of improving the quality of image datasets in the image retrieval system. Keywords: Image retrieval system, Local tri-directional pattern, Streching Image, Histogram Equalization, Similarity Measurement Canberra. Abstrak Local tri-directional pattern (LtriDP) merupakan salah satu metode ekstraksi fitur intensitas lokal dari setiap piksel berdasarkan arah. Namun, metode ini belum mampu memberikan performa yang baik dalam mengekstrak fitur untuk temu kembali citra. Salah satu alasan yang membuat performa temu kembali citra tidak baik adalah pengaruh pencahayaan. Pencahayaan dapat menyebabkan variasi besar antar citra. Penelitian ini mengusulkan pemanfaatan Histogram Equalization (HE). HE merupakan metode fungsional dalam peregangan derajat keabuan dan memperluas kontras citra. Hal ini akan membuat variasi level keabuan dari citra asli dapat terkendali. Ada beberapa tahapan utama dalam penelitian ini, yang pertama citra query dan citra dataset akan terlebih dahulu di preprocessing dengan histogram equalization. Setelah itu, citra tersebut diekstrak fiturnya, dicari pola tri-directional dan pola magnitude. Pola tri-directional akan menghasilkan dua histogram, sedangkan pola magnitude menghasilkan satu histogram. Ketiga histogram tersebut kemudian disatukan atau dilakukan joint histogram. Histogram yang telah dijoint merupakan vektor fitur. Vektor fitur tersebut akan dihitung rankingnya menggunakan pengukuran jarak canberra. Setelah itu, akan didapatkan citra yang mirip dengan citra query. Uji coba dilakukan dengan menggunakan 3 dataset wajah yaitu ORL, BERN, dan YALE. Nilai rata-rata recall yang di dapatkan 0,422 untuk dataset ORL, 0,50 untuk dataset BERN, dan 0,63 untuk dataset YALE. Dari hasil evaluasi tersebut, dapat disimpulkan metode yang diusulkan dapat digunakan sebagai proses peningkatan kualitas dataset citra pada system temu kembali citra. Keywords: Sistem Temu Kembali Citra, Local tri-directional pattern, Peregangan Kontras, Histogram Equalization, Perhitungan Jarak Canberra.
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Kadhum, Zainab Abdulrazzaq. "Equalize The Histogram Equalization for Image enhancement." Journal of Kufa for Mathematics and Computer 1, no. 5 (May 30, 2012): 14–21. http://dx.doi.org/10.31642/jokmc/2018/010502.

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Histogram Equalization is one of the technique most commonly used in contrast enhancement. it tends to change the mean brightness of the image to the middle level of the gray level range. However, In this paper, a simple contrast enhancement technique based on conventional histogram equalization algorithm is proposed. This Equalize The histogram equalization technique which takes control over the effect of  histogram equalization technique so that it performs the enhancement of an image
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Rudikov, S. I., V. Yu Tsviatkou, and A. P. Shkadarevich. "Dynamic range reduction of infrared images based on adaptive equalization, stretch and compression of histogram." Proceedings of the National Academy of Sciences of Belarus, Physical-Technical Series 66, no. 4 (December 26, 2021): 470–82. http://dx.doi.org/10.29235/1561-8358-2021-66-4-470-482.

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The problem of reducing the dynamic range and improving the quality of infrared (IR) images with a wide dynamic range for their display on a liquid crystal matrix with 8-bit pixels is considered. To solve this problem in optoelectronic devices in real time, block algorithms based on local equalization of the histogram are widely used, taking into account their relatively low computational complexity and the possibility of taking into account local features of the brightness distribution. The basic adaptive histogram equalization algorithm provides reasonably high image quality after conversion, but may result in excessive contrast for some types of images. In a modified algorithm of adaptive histogram equalization, the contrast is limited by a threshold by truncating local maxima at the edges of the histogram. This leads, however, to a deterioration in other indicators of image quality. This disadvantage is inherent in many algorithms of local histogram equalization, along with limited control over the characteristics of image reproduction quality. To improve the quality and expand the control interval for the characteristics of the reproduction of infrared images, the article proposes an algorithm for double reduction of the dynamic range of the image with intermediate control of the shape of its histogram. This algorithm performs: preliminary reduction of the dynamic range of the image based on adaptive equalization of the histogram, control of the shape of the histogram based on its linear or nonlinear compression, linear stretching of its central part and linear stretching (compression) of its lateral parts, final reduction of the dynamic range based on linear compression of the entire histograms. The characteristics of the proposed algorithm are compared with the characteristics of known algorithms for reducing the dynamic range and improving the image quality. The dependences of the characteristics of the quality of image reproduction after a decrease in their dynamic range on the control parameters of the proposed algorithm and recommendations for their choice taking into account the computational complexity are given.
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7

Tang, Jing Rui, and Nor Ashidi Mat Isa. "Bi-histogram equalization using modified histogram bins." Applied Soft Computing 55 (June 2017): 31–43. http://dx.doi.org/10.1016/j.asoc.2017.01.053.

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8

Wijaya Kusuma, I. Wayan Angga, and Afriliana Kusumadewi. "PENERAPAN METODE CONTRAST STRETCHING, HISTOGRAM EQUALIZATION DAN ADAPTIVE HISTOGRAM EQUALIZATION UNTUK MENINGKATKAN KUALITAS CITRA MEDIS MRI." Simetris: Jurnal Teknik Mesin, Elektro dan Ilmu Komputer 11, no. 1 (April 30, 2020): 1–10. http://dx.doi.org/10.24176/simet.v11i1.3153.

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Citra medis adalah suatu pola atau gambar dua dimensi bagian dalam tubuh manusia yang digunakan oleh ahli kesehatan untuk mendeteksi dan menganalisa penyakit pasien. Pada bidang radiologi citra yang sering digunakan saat ini adalah citra Magnetic resonance Imaging (MRI). Keunggulan citra MRI adalah kemampuan menampilkan detail anatomi secara jelas dalam berbagai potongan (multiplanar) tanpa mengubah posisi pasien. Citra MRI ini akan digunakan oleh dokter ataupun peneliti untuk melakukan analisis ada tidaknya suatu tumor, kanker, atau kelainan pada pasien. Penelitian ini mengusulkan metode Contrast Stretching, Histogram Equalization dan Adaptive Histogram Equalization untuk meningkatkan kualitas citra medis. Batasan masalah penelitian ini adalah citra medis MRI yang digunakan sebagai obyek penelitian adalah citra medis MRI Otak baik yang normal atau yang mengalami lesi (gangguan). Dari hasil kualitas citra dan analisa kuantitatif menunjukkan bahwa metode contrast stretching menghasilkan hasil kualitas citra MRI jauh lebih baik dibandingkan dengan metosde histogram equalization, dan adaptive histogram equalization. Nilai MSE yang paling rendah adalah pada metode contrast stretching yaitu 0,00346. Sedangkan nilai MSE yang paling besar dihasilkan oleh metode histogram equalization. Kualitas citra dengan metode contrast stretching menghasilkan nilai PSNR yang paling besar yaitu 22,0677. Ini menandakan bahwa kualitas citra dari metode contrast stretching jauh lebih baik dibandingkan metode histogram equalization, dan adaptive histogram equalization.
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Winarno, Guntur, Muhammad Irsal, Claricia Alamanda Karenina, Gando Sari, and Rinda Nur Hidayati. "Metode Histogram Equalization untuk Peningkatan Kualitas Citra dengan Menggunakan Studi Phantom Lumbosacral." Jurnal Kesehatan Vokasional 7, no. 2 (May 31, 2022): 104. http://dx.doi.org/10.22146/jkesvo.71469.

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Latar Belakang: Pemeriksaan lumbosacral sering kali menghasilkan kualitas citra yang kurang optimal. histogram equalization merupakan tahapan memanipulasi data citra digital untuk meningkatkan kualitas citra yang dapat diimplementasikan pada citra digital radiografi lumbosacral.Tujuan: Mengevaluasi peningkatan kualitas citra digital radiografi lumbosacral dengan menggunakan histogram equalization.Metode: Jenis penelitian ini adalah kuantitatif dengan pendekatan eksperimental. Jumlah sampel terdiri dari 1 Kyouku’s anthropomorphic phantom yang dibagi menjadi citra lumbosacral proyeksi antero posterior (AP) dan lateral sebelum dan setelah direkonstruksi menggunakan histogram equalization. Kualitas citra dinilai dengan analisis grafik histogram, pengukuran nilai signal to noise ratio (SNR) merupakan parameter untuk menentukan kualitas citra radiografi, dan visual grading analysis (VGA) oleh 10 orang radiografer dianalisis dengan menggunakan Uji Wilcoxon Signed-Rank.Hasil: Hasil kualitas citra menunjukkan bahwa analisis grafik histogram memiliki visual kecerahan yang meningkat, grafik histogram terdistribusi merata, dan nilai SNR meningkat setelah direkonstruksi dengan metode histogram equalization. Hasil VGA dengan menggunakan uji wilcoxon Signed-Rank setelah direkonstruksi kembali dengan metode histogram equalization pada proyeksi AP menunjukkan nilai 0,005 dan proyeksi lateral 0,074 dengan p-value > 0,05.Kesimpulan: Terjadi peningkatan kualitas citra radiografi proyeksi AP dan lateral dengan menggunakan metode histogram equalization.
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KWON, S. H., H. C. JEONG, S. T. SEO, I. K. LEE, and C. S. SON. "Histogram Equalization-Based Thresholding." IEICE Transactions on Information and Systems E91-D, no. 11 (November 1, 2008): 2751–53. http://dx.doi.org/10.1093/ietisy/e91-d.11.2751.

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11

Jbara, Wurood A., and Rafah A. Jaafar. "MRI Medical Images Enhancement based on Histogram Equalization and Adaptive Histogram Equalization." International Journal of Computer Trends and Technology 50, no. 2 (August 25, 2017): 91–93. http://dx.doi.org/10.14445/22312803/ijctt-v50p116.

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Jagatheeswari, P., S. Suresh Kumar, and M. Mary Linda. "Quadrant Dynamic with Automatic Plateau Limit Histogram Equalization for Image Enhancement." Mathematical Problems in Engineering 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/302732.

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The fundamental and important preprocessing stage in image processing is the image contrast enhancement technique. Histogram equalization is an effective contrast enhancement technique. In this paper, a histogram equalization based technique called quadrant dynamic with automatic plateau limit histogram equalization (QDAPLHE) is introduced. In this method, a hybrid of dynamic and clipped histogram equalization methods are used to increase the brightness preservation and to reduce the overenhancement. Initially, the proposed QDAPLHE algorithm passes the input image through a median filter to remove the noises present in the image. Then the histogram of the filtered image is divided into four subhistograms while maintaining second separated point as the mean brightness. Then the clipping process is implemented by calculating automatically the plateau limit as the clipped level. The clipped portion of the histogram is modified to reduce the loss of image intensity value. Finally the clipped portion is redistributed uniformly to the entire dynamic range and the conventional histogram equalization is executed in each subhistogram independently. Based on the qualitative and the quantitative analysis, the QDAPLHE method outperforms some existing methods in literature.
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Inoue, Kohei, Naoki Ono, and Kenji Hara. "Local Contrast-Based Pixel Ordering for Exact Histogram Specification." Journal of Imaging 8, no. 9 (September 10, 2022): 247. http://dx.doi.org/10.3390/jimaging8090247.

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Histogram equalization is one of the basic image processing tasks for contrast enhancement, and its generalized version is histogram specification, which accepts arbitrary shapes of target histograms including uniform distributions for histogram equalization. It is well known that strictly ordered pixels in an image can be voted to any target histogram to achieve exact histogram specification. This paper proposes a method for ordering pixels in an image on the basis of the local contrast of each pixel, where a Gaussian filter without approximation is used to avoid the duplication of pixel values that disturbs the strict pixel ordering. The main idea of the proposed method is that the problem of pixel ordering is divided into small subproblems which can be solved separately, and then the results are merged into one sequence of all ordered pixels. Moreover, the proposed method is extended from grayscale images to color ones in a consistent manner. Experimental results show that the state-of-the-art histogram specification method occasionally produces false patterns, which are alleviated by the proposed method. Those results demonstrate the effectiveness of the proposed method for exact histogram specification.
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Song, Yu Ting, Xiu Hua Ji, and Shi Lin Zhao. "An Improved Color Image Enhancement Algorithm Based on 3-D Color Histogram Equalization." Applied Mechanics and Materials 321-324 (June 2013): 1133–37. http://dx.doi.org/10.4028/www.scientific.net/amm.321-324.1133.

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This paper proposes an improved color image enhancement algorithm based on 3-D color histogram equalization algorithm. When the existed 3-D color histogram equalization algorithms in the literatures are applied in processing dim color images, the processed color images often turn pale due to the decrease of color-saturations and have false contours due to gray-scale merging phenomenon in the histogram equalization algorithm. In this paper, the proposed algorithm can make more pixels of the processed color images keep their color-saturations and reduce the gray-scale merging with Logarithmic histogram equalization algorithm. Test results with dim color images present a better effect of image enhancement.
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Surmayanti, Surmayanti, and Sumijan Sumijan. "Improving Digital Image Clarity: A Study on the Application of Histogram Equalization for Noise Correction." sinkron 8, no. 2 (April 9, 2024): 1073–79. http://dx.doi.org/10.33395/sinkron.v8i2.13564.

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This study aims to improve the clarity of digital images by examining the application of the histogram equalization method for noise correction. Noise in digital images is often a major challenge in maintaining the clarity and authenticity of visual information. Histogram equalization has been recognized as an effective method in improving image contrast and reducing the effects of noise. In this research, we conducted experiments by applying histogram equalization techniques to various types of digital images that are affected by noise. We analyzed the results by comparing the clarity and quality of the images before and after applying this method. The results of this research show that histogram equalization is able to significantly improve the clarity of digital images by reducing the effects of noise without sacrificing important details in the image. The implication of this discovery is the potential use of the histogram equalization method as an effective tool in improving the quality of digital images that are affected by noise.
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Peng, Na Xin, and Yu Qiang Chen. "Improved Self-Adaptive Image Histogram Equalization Algorithm." Advanced Materials Research 760-762 (September 2013): 1495–500. http://dx.doi.org/10.4028/www.scientific.net/amr.760-762.1495.

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Histogram equalization (HE) algorithm is wildly used method in image processing of contrast adjustment using images histogram. This method is useful in images with backgrounds and foreground that are both bright or both dark. But the performance of HE is not satisfactory to images with backgrounds and foregrounds that are both bright or both dark. To deal with the above problem, [ gives an improved histogram equalization algorithm named self-adaptive image histogram equalization (SIHE) algorithm. Its main idea is to extend the gray level of the image which firstly be processed by the classical histogram equalization algorithm. This paper gives detailed introduction to SIHE and analyzes the shortage of it, then give an improved version of SIHE named ISIHE, finally do experiments to show the performance of our algorithm.
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Sabarish, R. T., and R. Ramadevi. "Analysis and Comparison of Image Enhancement Technique for Improving PSNR of Lung Images by Linear Contrast Enhancement Technique over Histogram Equalization Technique." CARDIOMETRY, no. 25 (February 14, 2023): 838–44. http://dx.doi.org/10.18137/cardiometry.2022.25.838844.

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Aim: The aim of this study is to compare and analyze linear contrast enhancement algorithm for lung image enhancement over novel histogram equalization technique. Material and Methods: In this research, different sources of lung images collected from the kaggle website were used. Samples were considered as (N=30) for linear contrast filter and (N=30) for novel histogram equalization technique with total sample size calculated using clinical.com. As a result the total number of samples was calculated to be 60. Using SPSS Software and a standard data set, the PSNR was obtained. Both linear contrast filter and novel histogram equalization technique image enhancement were implemented on lung images through Matlab coding and also PSNR values of each image were extracted. Then through SPSS software comparison and analysis has been made. Result: In the final output of image enhancement, novel histogram equalization technique shows better performance in improving PSNR of lung images than linear contrast filter. Comparison of PSNR values are done by independent sample test using IBM-SPSS software. There is a statistical difference between histogram technique and linear contrast filter. The novel histogram equalization technique showed higher results of PSNR (65.9197dB) with (p=0.04) in comparison with linear contrast filter (36.1190dB). Conclusion: Within this research study the histogram equalization image enhancement technique has greater PSNR value of lung images than in linear contrast enhancement technique.
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Santhi, K., Wahida Banu, and R. Dhanasekaran. "Contrast Enhanced for Microstructure of Steel Materials and Engine Components." Advanced Materials Research 984-985 (July 2014): 1375–79. http://dx.doi.org/10.4028/www.scientific.net/amr.984-985.1375.

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This paper analyses the contrast of qualitative and quantitative of piston and steel microstructure images. A simple discrimination metric (DMHE) is developed to avoid the drawbacks of conventional histogram equalization for gray scale images. The proposed technique uses both global and local information to remap the intensity levels that help to improve the image contrast. The original histogram is divided into sub-histograms with respect to the mean value. Discrimination metrics are used so that high contrast per pixel between real image and upgraded image is obtained. The simulation results show that the proposed method performed well for mechanical component material of piston and steel microstructure images. Parameters like structural similarity index and contrast per pixel are used to analyze the image quality. Keywords-Piston and Steel microstructure, Contrast Enhancement, Two level Histogram Equalization, Discrimination Metric.
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Mustaghfirin, Fathan, Erwin, Hadrians Kesuma Putra, Umi Yanti, and Rahma Ricadonna. "The Comparison of Iris Detection Using Histogram Equalization and Adaptive Histogram Equalization Methods." Journal of Physics: Conference Series 1196 (March 2019): 012016. http://dx.doi.org/10.1088/1742-6596/1196/1/012016.

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Bhaskara Rao, Jana, K. V G Srinivas, A. Siva kumar, and J. Beatrice Seventline. "Bi Histogram Equalization Based Image Enhancement with Bicubic Interpolation." ECS Transactions 107, no. 1 (April 24, 2022): 1441–57. http://dx.doi.org/10.1149/10701.1441ecst.

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In image processing, enhancement histogram equalization is the widely used technique for contrast enhancement. However, this technique tends to change the brightness of the image. Here, the contrast and resolution of image were enhanced using the proposed Bi Histogram Equalization Based Image Enhancement with Bicubic Interpolation (BHBI) technique. Bi Histogram for contrast enhancement and bicubic interpolation for resolution enhancement has taken. Bi Histogram Equalization separates the input image's histogram into two based on input mean before equalizing them independently. Bicubic Interpolation can generate bigger or high-resolution image from one or more low resolution or smaller images. The performance of the BHBI method can be compared for some typical image’s cameraman lens that are applied to some existing enhancement methods like Adaptive Gamma Correction with Weighting Distribution (AGCWD), Adaptive Scale Adjustment Design of Unsharp Masking Filters (ASAUMF), Averaging Histogram Equalization (AVGHEQ), and Median-Mean Based Sub-Image-Clipped Histogram Equalization (MMSICHE). The performance of these existing techniques can be evaluated subjectively in terms of person illustration observation and measurably using Discrete Entropy (DE), Image Quality Index (IQI), Normalized Correlation Coefficient (NCC), Contrast Improvement Index (CII), and Absolute Mean Brightness Error (AMBE). The results obtained from the BHBI technique shows better when compared with respect to the various existing techniques.
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Sabarish, R. T., and R. Ramadevi. "Analysis and Comparison of Image Enhancement Technique for Improving PSNR of Lung Images by Wiener Filtering over Histogram Equalization Technique." CARDIOMETRY, no. 25 (February 14, 2023): 832–37. http://dx.doi.org/10.18137/cardiometry.2022.25.832837.

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Aim: The aim of this study is to compare and analyze wiener filter algorithm for lung image enhancement over novel histogram equalization technique. Materials and Methods:In this research, different sources of lung images collected from the kaggle website were used. Samples were considered as (N=30) for median filtering and (N=30) for novel histogram equalization technique with total sample size calculated using clinical. com. As a result the total number of samples was calculated to be 60. Using SPSS Software and a standard data set, the PSNR was obtained. Both median filter and novel histogram equalization technique image enhancement were implemented on lung images through Matlab coding and also extracting PSNR values of each image. Then through SPSS software comparison and analysis has been made. Result:. The novel histogram equalization technique showed higher results of PSNR (67.2076dB) in comparison with wiener filtering (37.1558dB). It is observed that histogram algorithm performed better than the Wiener filter (p=0.04) by performing an independent sample t-test. Conclusion: Within this research study the histogram equalization image enhancement technique has greater PSNR value of lung images than in wiener filtering technique.
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Oliver, William R. "Histogram Stretching Or Histogram Equalization In Image Processing." Microscopy Today 6, no. 3 (April 1998): 20–24. http://dx.doi.org/10.1017/s1551929500066797.

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A few weeks ago, a person posted an interesting question on an internet microscopy mailing list: what is the difference between histogram stretching and histogram equalization when applied to microscopy images? The following is a short intuitive review which compares the two. Since this is a short and general description, I will gloss over some details and make some generalizations which may not be true in all cases and implementations.The first thing to remember is the basic purpose of contrast enhancement. The idea is simple, In a grayscale (black and white) image you are simply trying to take two levels of gray that are close together, and thus visually similar, and move them apart so you can better see the difference between them. You can think of grayscale values in an image as beads on a string, Contrast enhancement simply moves all or some of those beads farther apart.
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Sabarish, R. T., and R. Ramadevi. "Analysis and Comparison of Image Enhancement Technique for Improving PSNR of Lung Images by Unsharp Mask Filtering Technique over Histogram Equalization Technique." CARDIOMETRY, no. 25 (February 14, 2023): 825–31. http://dx.doi.org/10.18137/cardiometry.2022.25.825831.

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Aim: The goal of study in this image enhancement technique is to enhance both contrast and sharpness of an image simultaneously to improve PSNR. Materials and Methods: Both unsharp mask filter and novel histogram equalization techniques were implemented on lung images which were collected from kaggle software. Samples were considered as (N=30) for unsharp mask filtering and (N=30) for novel histogram equalization technique with total sample size calculated using clinical. com. As a result the total number of samples was calculated as 60. Matlab coding was written for extracting PSNR values of each image. Comparison and analysis has been made through SPSS software. Results: In the final output of image enhancement, novel histogram equalization technique shows better performance in improving PSNR of lung images than unsharp mask filtering technique. Comparison of PSNR values are done by independent sample test using IBM-SPSS software. There is a statistical difference between histogram technique and unsharp mask filtering. The novel histogram equalization technique showed higher results of PSNR (67.2860dB) with (p=0.04) in comparison with unsharp mask filtering (37.9313dB). Conclusion: Within this research study histogram equalization image enhancement technique has greater PSNR value of lung images than unsharp mask filtering technique.
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Ye, Bowen, Sun Jin, Bing Li, Shuaiyu Yan, and Deng Zhang. "Dual Histogram Equalization Algorithm Based on Adaptive Image Correction." Applied Sciences 13, no. 19 (September 25, 2023): 10649. http://dx.doi.org/10.3390/app131910649.

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For the visual measurement of moving arm holes in complex working conditions, a histogram equalization algorithm can be used to improve image contrast. To lessen the problems of image brightness shift, image over-enhancement, and gray-level merging that occur with the traditional histogram equalization algorithm, a dual histogram equalization algorithm based on adaptive image correction (AICHE) is proposed. To prevent luminance shifts from occurring during image equalization, the AICHE algorithm protects the average luminance of the input image by improving upon the Otsu algorithm, enabling it to split the histogram. Then, the AICHE algorithm uses the local grayscale correction algorithm to correct the grayscale to prevent the image over-enhancement and gray-level merging problems that arise with the traditional algorithm. It is experimentally verified that the AICHE algorithm can significantly improve the histogram segmentation effect and enhance the contrast and detail information while protecting the average brightness of the input image, and thus the image quality is significantly increased.
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Yang, Guo Liang, Zhi Lin Cheng, and Li Zhang. "Skin Detection Model Research Based on Image Enhancement." Applied Mechanics and Materials 65 (June 2011): 260–63. http://dx.doi.org/10.4028/www.scientific.net/amm.65.260.

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Histogram element gradation distribution of the original image concentrates in the low gradation level, and after histogram equalization processed the image is bright and unconspicuous in details. In order to improve the situation, this paper presents an improved image enhancement algorithm .In the algorithm the original image is transformed by the conventional histogram equalization and mapped the histogram equalization processing image as far as possible within the scope of mapping. Then linear transform is used to enhance contrast and apply to mix skin complexion model to extract. Experiments prove that this method is better than double skin model detection at testing results, especially in the eyes and mouth.
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Muhammad, Fadhel, Dhila Aprilianti, Annisa Amanda Nelvi, Aulia Khairunisa, Muhamad Restu Inyasdi Kahvi, Endang Purnama Giri, and Faldiena Marcelita. "Perbaikan Kualitas Citra Cahaya Redup Menggunakan Teknik Perbaikan Histogram Equalization dan Adaptive Multi-scale Retinex." Jurnal Ilmu Komputer dan Agri-Informatika 11, no. 1 (May 31, 2024): 19–26. http://dx.doi.org/10.29244/jika.11.1.19-26.

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Citra low light seringkali memiliki kualitas yang rendah, dengan kurangnya cahaya yang menyebabkan citra yang gelap, kontras rendah, dan detail yang terabaikan. Dalam upaya untuk meningkatkan citra low light berbagai metode telah dikembangkan, termasuk histogram equalization dan adaptive multi-scale retinex (AMSR). Dari kedua metode ini, belum ada kesepakatan mengenai mana yang lebih efektif dalam perbaikan citra low light. Dalam penelitian ini, kami membandingkan kinerja metode histogram equalization dan AMSR dalam perbaikan citra low light. Metode histogram equalization diterapkan untuk mengubah distribusi intensitas piksel dalam citra. Histogram equalization memiliki kelemahan dalam mempertahankan kontras lokal dan dapat menghasilkan citra yang terlalu tajam. Selanjutnya, metode AMSR diterapkan untuk memperbaiki kontras dan detail citra low light. Dalam penelitian ini, AMSR diterapkan dengan skala adaptif pada berbagai tingkat deteksi. Hasil penelitian menunjukkan bahwa kedua metode memiliki kemampuan untuk meningkatkan citra low light. Metode histogram equalization memberikan peningkatan yang signifikan dalam kontras global dan kecerahan citra, sementara metode AMSR berhasil mempertahankan kontras lokal dan detail citra. Perbedaan juga terjadi pada hasil yang diperoleh, tergantung pada karakteristik citra dan preferensi pengguna. Berdasarkan analisis dan evaluasi yang dilakukan, dapat disimpulkan bahwa kedua metode memiliki kelebihan dan kelemahan masing-masing dalam perbaikan citra low light.
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Lehr, J. L., and P. Capek. "Histogram equalization of CT images." Radiology 154, no. 1 (January 1985): 163–69. http://dx.doi.org/10.1148/radiology.154.1.3964935.

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Inoue, Kohei, Kenji Hara, and Kiichi Urahama. "Gradient Norm-Based Histogram Equalization." Journal of The Institute of Image Information and Television Engineers 67, no. 8 (2013): J296—J299. http://dx.doi.org/10.3169/itej.67.j296.

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Chung, Soyoung, and Min Gyo Chung. "Histogram Equalization using Gamma Transformation." KIISE Transactions on Computing Practices 20, no. 12 (December 15, 2014): 646–51. http://dx.doi.org/10.5626/ktcp.2014.20.12.646.

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Khan, Mohammad Farhan, Xueshi Ren, and Ekram Khan. "Semi dynamic fuzzy histogram equalization." Optik 126, no. 21 (November 2015): 2848–53. http://dx.doi.org/10.1016/j.ijleo.2015.07.036.

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Daumas-Ladouce, Federico, Miguel García-Torres, José Luis Vázquez Noguera, Diego P. Pinto-Roa, and Horacio Legal-Ayala. "Multi-Objective Pareto Histogram Equalization." Electronic Notes in Theoretical Computer Science 349 (June 2020): 3–23. http://dx.doi.org/10.1016/j.entcs.2020.02.010.

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Stark, J. A., and W. J. Fitzgerald. "Model-based adaptive histogram equalization." Signal Processing 39, no. 1-2 (September 1994): 193–200. http://dx.doi.org/10.1016/0165-1684(94)90133-3.

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Adhinata, Faisal Dharma, Ariq Cahya Wardhana, Diovianto Putra Rakhmadani, and Akhmad Jayadi. "Peningkatan Kualitas Citra pada Citra Digital Gelap." Jurnal E-Komtek (Elektro-Komputer-Teknik) 4, no. 2 (December 25, 2020): 136–44. http://dx.doi.org/10.37339/e-komtek.v4i2.373.

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Salah satu tahap utama dalam pemrosesan citra digital adalah peningkatan kualitas citra. Citra yang berwarna gelap tidak terlihat detail informasi yang terkandung pada citra. Bahkan objek yang tampak pada citra bisa tidak terlihat karena pengambilan citra dilakukan pada pencahayaan kurang. Citra gelap perlu dilakukan peningkatan kualitas citra supaya detail informasi citra dapat terlihat secara visual. Beberapa algoritma peningkatan kualitas citra digital diantaranya negative transformation, log transformation, contrast stretching, bit plane slice, dan histogram equalization. Pada penelitian ini akan dikaji beberapa algoritma peningkatan kualitas citra untuk melihat hasil terbaik dari kasus citra gelap. Berdasarkan hasil percobaan, diperoleh hasil terbaik menggunakan algoritma histogram equalization. Algoritma histogram equalization menghasilkan histogram citra yang tersebar rata sehingga detail informasi citra dapat dilihat secara visual.
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Murali, V., and T. Venkateswarlu. "A Novel Technique for Automatic Image Enhancement using HTHET Approach." Asian Journal of Computer Science and Technology 8, no. 1 (February 5, 2019): 26–31. http://dx.doi.org/10.51983/ajcst-2019.8.1.2123.

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Image enhancement techniques are methods used for producing images with better quality than the original image. None of the existing methods increase the information content of the image, and are usually of little interest for subsequent automatic analysis of images. In this paper, automated Image Enhancement is achieved by carrying out Histogram techniques. Histogram equalization (HE) is a spatial domain image enhancement technique, which effectively enhances the contrast of an image. We make use of Transformation and Hyperbolization techniques for automatic image enhancement. However, while it takes care of contrast enhancement, a modified histogram equalization technique, Histogram Transformation and Hyperbolization Equalization Technique (HTHET) using optimization method is proposed using EQHIST and LINHIST.
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Adhinata, Faisal Dharma, Ariq Cahya Wardhana, Diovianto Putra Rakhmadani, and Akhmad Jayadi. "Peningkatan Kualitas Citra pada Citra Digital Gelap." Jurnal E-Komtek (Elektro-Komputer-Teknik) 4, no. 2 (December 25, 2020): 136–44. http://dx.doi.org/10.37339/e-komtek.v4i2.373.

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Salah satu tahap utama dalam pemrosesan citra digital adalah peningkatan kualitas citra. Citra yang berwarna gelap tidak terlihat detail informasi yang terkandung pada citra. Bahkan objek yang tampak pada citra bisa tidak terlihat karena pengambilan citra dilakukan pada pencahayaan kurang. Citra gelap perlu dilakukan peningkatan kualitas citra supaya detail informasi citra dapat terlihat secara visual. Beberapa algoritma peningkatan kualitas citra digital diantaranya negative transformation, log transformation, contrast stretching, bit plane slice, dan histogram equalization. Pada penelitian ini akan dikaji beberapa algoritma peningkatan kualitas citra untuk melihat hasil terbaik dari kasus citra gelap. Berdasarkan hasil percobaan, diperoleh hasil terbaik menggunakan algoritma histogram equalization. Algoritma histogram equalization menghasilkan histogram citra yang tersebar rata sehingga detail informasi citra dapat dilihat secara visual.
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Yustiantara, Natanael Putra. "IMAGE ENHACEMENT PADA CITRA GESTUR TANGAN MENGGUNAKAN CONTRAST LIMITED ADAPTIVE HISTOGRAM EQUALIZATION." Joutica 6, no. 2 (September 13, 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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Magudeeswaran, V., and C. G. Ravichandran. "Fuzzy Logic-Based Histogram Equalization for Image Contrast Enhancement." Mathematical Problems in Engineering 2013 (2013): 1–10. http://dx.doi.org/10.1155/2013/891864.

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Fuzzy logic-based histogram equalization (FHE) is proposed for image contrast enhancement. The FHE consists of two stages. First, fuzzy histogram is computed based on fuzzy set theory to handle the inexactness of gray level values in a better way compared to classical crisp histograms. In the second stage, the fuzzy histogram is divided into two subhistograms based on the median value of the original image and then equalizes them independently to preserve image brightness. The qualitative and quantitative analyses of proposed FHE algorithm are evaluated using two well-known parameters like average information contents (AIC) and natural image quality evaluator (NIQE) index for various images. From the qualitative and quantitative measures, it is interesting to see that this proposed method provides optimum results by giving better contrast enhancement and preserving the local information of the original image. Experimental result shows that the proposed method can effectively and significantly eliminate washed-out appearance and adverse artifacts induced by several existing methods. The proposed method has been tested using several images and gives better visual quality as compared to the conventional methods.
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Kaesardi, Dinda Septika, Abduh Riski, and Ahmad Kamsyakawuni. "Perbaikan Citra Inframerah dengan Metode Divide-Conquer dan Metode Histogram Equalization." BERKALA SAINSTEK 6, no. 2 (December 13, 2018): 71. http://dx.doi.org/10.19184/bst.v6i2.9226.

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CCTV (Closed Circuit Television) atau kamera pengawas yang berbasis inframerah banyak dijumpai di tempat-tempat umum seperti persimpangan jalan, perkantoran, pertokoan, dll. Inframerah merupakan suatu radiasi elektromagnetik yang di dalam kamera CCTV berfungsi untuk mengadaptasi gambar dalam keadaan kurang cahaya menjadi terlihat oleh mata dalam mode grayscale. Namun, citra inframerah ini mengalami sedikit derau (noise), kurang tajam, kabur, dsb. Sehingga diperlukan suatu proses perbaikan citra. Penelitian ini akan membahas perbandingan metode Histogram Equalization dan Divide-Conquer, kemudian kedua citra hasil dibandingkan berdasarkan visual dan Liniear Index of Fuzziness. Berdasarkan hasil penelitian, metode Divide-Conquer menghasilkan kualitas citra yang lebih baik secara visual ataupun dengan Linear Index of Fuzziness dibanding dengan Histogram Equalization. Jika dengan dibandingkan dengan citra asli, kedua metode menghasilkan citra yang lebih baik. Namun, hasil citra Histogram Equalization lebih terang sehingga ada beberapa detail citra yang hilang. Kata Kunci: Perbaikan citra, citra inframerah, Histogram Equalization, Divide-Conquer, Linear Index of Fuzziness.
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Yuan, Yan Bin, Bo Zhang, Xiao Hui Yuan, Xiao Pan Zhang, Wen Zhao Xia, and Ying Chen. "An Improved Algorithm of Histogram Equalization to Increase Brightness of Image in Mine." Applied Mechanics and Materials 204-208 (October 2012): 4789–93. http://dx.doi.org/10.4028/www.scientific.net/amm.204-208.4789.

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To improve the contrast ratio and brightness of video surveillance image in mine, we modify the histogram equalization algorithm by extent the boundary when calculating the sum of gray probability. Experimental results show that the algorithm can reserve the characteristics of the histogram equalization and improve the image’s brightness.
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Lawton, Sahil, and Serestina Viriri. "Detection of COVID-19 from CT Lung Scans Using Transfer Learning." Computational Intelligence and Neuroscience 2021 (April 8, 2021): 1–14. http://dx.doi.org/10.1155/2021/5527923.

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This paper aims to investigate the use of transfer learning architectures in the detection of COVID-19 from CT lung scans. The study evaluates the performances of various transfer learning architectures, as well as the effects of the standard Histogram Equalization and Contrast Limited Adaptive Histogram Equalization. The findings of this study suggest that transfer learning-based frameworks are an alternative to the contemporary methods used to detect the presence of the virus in patients. The highest performing model, the VGG-19 implemented with the Contrast Limited Adaptive Histogram Equalization, on a SARS-CoV-2 dataset, achieved an accuracy and recall of 95.75% and 97.13%, respectively.
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Ibrahim, Haidi, and Seng Chun Hoo. "Local Contrast Enhancement Utilizing Bidirectional Switching Equalization of Separated and Clipped Subhistograms." Mathematical Problems in Engineering 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/848615.

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Digital image contrast enhancement methods that are based on histogram equalization technique are still useful for the use in consumer electronic products due to their simple implementation. However, almost all the suggested enhancement methods are using global processing technique, which does not emphasize local contents. Therefore, this paper proposes a new local image contrast enhancement method, based on histogram equalization technique, which not only enhances the contrast, but also increases the sharpness of the image. Besides, this method is also able to preserve the mean brightness of the image. In order to limit the noise amplification, this newly proposed method utilizes local mean-separation, and clipped histogram bins methodologies. Based on nine test color images and the benchmark with other three histogram equalization based methods, the proposed technique shows the best overall performance.
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Aziz, Muhammad Abdul, Resty Wulanningrum, and Daniel Swanjaya. "STUDI PERBANDINGAN PERBAIKAN KUALITAS CITRA GESTUR TANGAN MENGGUNAKAN METODE HISTOGRAM EQUALIZATION DENGAN ADAPTIVE HISTOGRAM EQUALIZATION." Network Engineering Research Operation 6, no. 2 (November 22, 2021): 161. http://dx.doi.org/10.21107/nero.v6i2.239.

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Rudikov, S. I., V. Yu Tsviatkou, and A. P. Shkadarevich. "Reducing the dynamic range of infrared images based on block-priority equalization and compression of histograms." Informatics 19, no. 2 (April 11, 2022): 7–25. http://dx.doi.org/10.37661/1816-0301-2022-19-2-7-25.

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Objectives. The problem of reducing the dynamic range of infrared images for their reproduction on display devices with a narrow dynamic range is considered. The method of local image histogram equalization based on the integral distribution function of brightness is investigated. To transform the brightness of a pixel, this method uses an approximation of the local alignment values of the nearest blocks of pixels of original image. This in-creases the local contrast of the image, but leads to high computational complexity, which is increasing while block size decreases. The aim of the work is to reduce the computational complexity of adaptive equalization and compression of infrared image histograms while reducing their dynamic range.Methods. Image processing methods are used.Results. To reduce the computational complexity of transforming the dynamic range of infrared images, a block-priority modification of the adaptive histogram equalization method is proposed. The modification is based on the division of the set of image blocks into two subsets of high-priority and low-priority blocks depend-ing on their brightness statistical properties. When interpolating pixel values, high-priority blocks use local alignment values, and low-priority blocks use global alignment values. As a result, the total number of alignment vectors is reduced in proportion to the ratio of subsets sizes and the computational complexity of the dynamic range transformation is reduced.Conclusion. When changing the ratio of the number of high-priority blocks of infrared image pixels to the number of all blocks in the range of 0.25–0.75, the proposed algorithm is more efficient than global and adaptive histogram equalization algorithms.
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Zhao, Yu Qian, and Zhi Gang Li. "FPGA Implementation of Real-Time Adaptive Bidirectional Equalization for Histogram." Advanced Materials Research 461 (February 2012): 215–19. http://dx.doi.org/10.4028/www.scientific.net/amr.461.215.

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According to the characteristics of infrared images, a contrast enhancement algorithm was presented. The principium of FPGA-based adaptive bidirectional plateau histogram equalization was given in this paper. The plateau value was obtained by finding local maximum and whole maximum in statistical histogram based on dimensional histogram statistic. Statistical histogram was modified by the plateau value and balanced in gray scale and gray spacing. Test data generated by single frame image, which was simulated by FPGA-based real-time adaptive bidirectional plateau histogram equalization. The simulation results indicates that the precept meet the requests well in both the image processing effects and processing speed
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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 (August 8, 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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Román, Julio César Mello, Vicente R. Fretes, Carlos G. Adorno, Ricardo Gariba Silva, José Luis Vázquez Noguera, Horacio Legal-Ayala, Jorge Daniel Mello-Román, Ricardo Daniel Escobar Torres, and Jacques Facon. "Panoramic Dental Radiography Image Enhancement Using Multiscale Mathematical Morphology." Sensors 21, no. 9 (April 29, 2021): 3110. http://dx.doi.org/10.3390/s21093110.

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Panoramic dental radiography is one of the most used images of the different dental specialties. This radiography provides information about the anatomical structures of the teeth. The correct evaluation of these radiographs is associated with a good quality of the image obtained. In this study, 598 patients were consecutively selected to undergo dental panoramic radiography at the Department of Radiology of the Faculty of Dentistry, Universidad Nacional de Asunción. Contrast enhancement techniques are used to enhance the visual quality of panoramic dental radiographs. Specifically, this article presents a new algorithm for contrast, detail and edge enhancement of panoramic dental radiographs. The proposed algorithm is called Multi-Scale Top-Hat transform powered by Geodesic Reconstruction for panoramic dental radiography enhancement (MSTHGR). This algorithm is based on multi-scale mathematical morphology techniques. The proposal extracts multiple features of brightness and darkness, through the reconstruction of the marker (obtained by the Top-Hat transformation by reconstruction) starting from the mask (obtained by the classic Top-Hat transformation). The maximum characteristics of brightness and darkness are added to the dental panoramic radiography. In this way, the contrast, details and edges of the panoramic radiographs of teeth are improved. For the tests, MSTHGR was compared with the following algorithms: Geodesic Reconstruction Multiscale Morphology Contrast Enhancement (GRMMCE), Histogram Equalization (HE), Brightness Preserving Bi-Histogram Equalization (BBHE), Dual Sub-Image Histogram Equalization (DSIHE), Minimum Mean Brightness Error Bi-Histogram Equalization (MMBEBHE), Quadri-Histogram Equalization with Limited Contrast (QHELC), Contrast-Limited Adaptive Histogram Equalization (CLAHE) and Gamma Correction (GC). Experimentally, the numerical results show that the MSTHGR obtained the best results with respect to the Contrast Improvement Ratio (CIR), Entropy (E) and Spatial Frequency (SF) metrics. This indicates that the algorithm performs better local enhancements on panoramic radiographs, improving their details and edges.
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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 (January 15, 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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Sabarish, R. T., and R. Ramadevi. "Analysis and Comparison of Image Enhancement Technique for Improving PSNR of Lung Images by Median Filtering over Histogram Equalization Technique." CARDIOMETRY, no. 25 (February 14, 2023): 818–24. http://dx.doi.org/10.18137/cardiometry.2022.25.818824.

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Aim: The main goal of this project is image enhancement to improve interpretability or perception of information in images for human viewers and also to provide better input for other automated image processing techniques. Materials and Methods: In this research different sources of lung images collected from Kaggle website were used. Samples were considered as (N=30) for median filtering and (N=30) for novel histogram equalization technique with total sample size calculated using clinical.com. As a result the total number of sample was calculated to be 60.Using SPSS Software and a standard data set,the PSNR was obtained. Both median filter and novel histogram technique image enhancement were implemented on Lung images through Matlab coding and also extracting PSNR values of each image. Then through SPSS software comparison and analysis has been made Results: In an image enhancement of the image processing pathway, novel histogram equalization technique shows the best performance by removing noise to improve PSNR of lung images than median filtering. Comparison of PSNR values are done by independent sample test using IBM-SPSS software. There is a statistical difference between histogram technique and median filtering. The novel histogram equalization technique showed higher results of PSNR (69.6557dB) with (p=0.04) in comparison with median filtering (37.6427dB). Conclusion: Histogram equalization image enhancement technique provides high PSNR values for different sources of lung images than median filtering Technique.
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Yan, Peng, Wu Zhan, and Ou Yang Min-Zi. "Histogram Equalization Based on Rough Set." Applied Mechanics and Materials 182-183 (June 2012): 1844–48. http://dx.doi.org/10.4028/www.scientific.net/amm.182-183.1844.

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In digital image processing, classical histogram equalization produce the loss of image information the caused by gray level of the output image may be too much merged. This paper mainly based on the concepts of the set approximate, classification approximate measurement and importance in the rough set theory, divided the appropriate boundary of the set, proposed an improved histogram equalization method, thus effectively solved the problem, gave the experimental simulation confirmation.
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Muhammad Abubakar, Fari. "Image Enhancement using Histogram Equalization and Spatial Filtering." International Journal of Science and Research (IJSR) 1, no. 3 (March 5, 2012): 15–20. http://dx.doi.org/10.21275/ijsr12120366.

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