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Journal articles on the topic 'Clipped Adaptive Histogram Equalization'

Author: Grafiati

Published: 5 June 2025

Last updated: 15 July 2025

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1

Kim, Taekyung, and Joonki Paik. "Adaptive contrast enhancement using gain-controllable clipped histogram equalization." IEEE Transactions on Consumer Electronics 54, no. 4 (2008): 1803–10. http://dx.doi.org/10.1109/tce.2008.4711238.

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2

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 (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 Interpol

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Jenifer, Sheeba, S. Parasuraman, and Amudha Kadirvelu. "Contrast enhancement and brightness preserving of digital mammograms using fuzzy clipped contrast-limited adaptive histogram equalization algorithm." Applied Soft Computing 42 (May 2016): 167–77. http://dx.doi.org/10.1016/j.asoc.2016.01.039.

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4

Deepa, Abin, and D. Thepade Sudeep. "Video Frame Illumination Inconsistency Reduction using CLAHE with Kekre's LUV Color Space." International Journal of Engineering and Advanced Technology (IJEAT) 9, no. 3 (2020): 620–24. https://doi.org/10.35940/ijeat.C5322.029320.

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Visual frame quality is of utmost significance and is relevant in numerous computer vision applications such as object detection, video surveillance, optical motion capture, multimedia and human computer interface. Under controlled or uncontrolled environment, the video visual frame quality gets affected due to illumination variations. This may further hamper the interpretability and may lead to significant loss of information for background modeling. An excellent background model can enhance good visual perception. In this work, local enhancement technique with improved background modeling, C

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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 re

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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

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7

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

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Baghel, Smita, and Anupam Shukla. "AElm-XGB: Hybrid Machine Learning Based Automated Classification of Oral Cancer by Effective Segmentation." Journal of Neonatal Surgery 14, no. 1S (2025): 1174–92. https://doi.org/10.52783/jns.v14.1718.

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Medical professionals benefit from early cancer disease detection because it shortens the time between diagnosis and treatment. In small towns and rural areas where people are less aware of the risks associated with tobacco and cigarette use, OC may have a high mortality rate due to its severe effects. The technique is insufficient for detecting OC disease in its early stages. Since people only live for five years on average after being diagnosed with OC disease, its detection may be possible at a later stage. By overcoming certain demerits of existing works, the proposed research work present

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Kanika, Kapoor, and Arora Shaveta. "COLOUR IMAGE ENHANCEMENT BASED ON HISTOGRAM EQUALIZATION." Electrical & Computer Engineering: An International Journal (ECIJ) 4, no. 3 (2015): 73–82. https://doi.org/10.5281/zenodo.3593819.

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Histogram equalization is a nonlinear technique for adjusting the contrast of an image using its histogram. It increases the brightness of a gray scale image which is different from the mean brightness of the original image. There are various types of Histogram equalization techniques like Histogram Equalization, Contrast Limited Adaptive Histogram Equalization, Brightness Preserving Bi Histogram Equalization, Dualistic Sub Image Histogram Equalization, Minimum Mean Brightness Error Bi Histogram Equalization, Recursive Mean Separate Histogram Equalization and Recursive Sub Image Histogram Equa

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10

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

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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 (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 Cont

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12

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

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13

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

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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 (2017): 91–93. http://dx.doi.org/10.14445/22312803/ijctt-v50p116.

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15

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 (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

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16

Reddy, Eswar, and Ramachandra Reddy. "Dynamic Clipped Histogram Equalization Technique for Enhancing Low Contrast Images." Proceedings of the National Academy of Sciences, India Section A: Physical Sciences 89, no. 4 (2018): 673–98. http://dx.doi.org/10.1007/s40010-018-0530-6.

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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 C

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18

Stimper, Vincent, Stefan Bauer, Ralph Ernstorfer, Bernhard Scholkopf, and Rui Patrick Xian. "Multidimensional Contrast Limited Adaptive Histogram Equalization." IEEE Access 7 (2019): 165437–47. http://dx.doi.org/10.1109/access.2019.2952899.

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19

Pizer, Stephen M., E. Philip Amburn, John D. Austin, et al. "Adaptive histogram equalization and its variations." Computer Vision, Graphics, and Image Processing 39, no. 3 (1987): 355–68. http://dx.doi.org/10.1016/s0734-189x(87)80186-x.

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20

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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21

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 sim

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22

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 Equali

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23

Singh, Kuldeep, and Rajiv Kapoor. "Image enhancement via Median-Mean Based Sub-Image-Clipped Histogram Equalization." Optik 125, no. 17 (2014): 4646–51. http://dx.doi.org/10.1016/j.ijleo.2014.04.093.

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24

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 i

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Muhammad, Fadhel, Dhila Aprilianti, Annisa Amanda Nelvi, et al. "Perbaikan Kualitas Citra Cahaya Redup Menggunakan Teknik Perbaikan Histogram Equalization dan Adaptive Multi-scale Retinex." Jurnal Ilmu Komputer dan Agri-Informatika 11, no. 1 (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 meng

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Gulmira, Omarova, Aitkozha Zhangeldi, Sadirmekova Zhanna, et al. "Devising a methodology for X-ray image contrast enhancement by combining CLAHE and gamma correction." Eastern-European Journal of Enterprise Technologies 3, no. 2 (117) (2022): 18–29. https://doi.org/10.15587/1729-4061.2022.258092.

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Increasing image contrast is very important for the visual analysis of X-ray images. To improve the contrast of medical images, various contrast enhancement methods are used, such as histogram equalization and histogram modifications, gamma correction, etc. The paper explores adaptive methods for enhancing the contrast of digital X-ray images. Research was carried out on 1000 images from the open Kaggle database. Combinations of sequential application of several methods for enhancing image contrast were evaluated. Experiments using gamma image correction allowed us to select ranges of input an

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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

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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 hist

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29

Xianfang Sun, P. L. Rosin, R. R. Martin, and F. C. Langbein. "Bas-Relief Generation Using Adaptive Histogram Equalization." IEEE Transactions on Visualization and Computer Graphics 15, no. 4 (2009): 642–53. http://dx.doi.org/10.1109/tvcg.2009.21.

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Buzuloiu, Vasile. "Adaptive-neighborhood histogram equalization of color images." Journal of Electronic Imaging 10, no. 2 (2001): 445. http://dx.doi.org/10.1117/1.1353200.

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31

Santhi, K., and R. S. D. Wahida Banu. "Adaptive contrast enhancement using modified histogram equalization." Optik - International Journal for Light and Electron Optics 126, no. 19 (2015): 1809–14. http://dx.doi.org/10.1016/j.ijleo.2015.05.023.

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Sherrier, Robert H., and G. A. Johnson. "Regionally Adaptive Histogram Equalization of the Chest." IEEE Transactions on Medical Imaging 6, no. 1 (1987): 1–7. http://dx.doi.org/10.1109/tmi.1987.4307791.

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33

Paranjape, Raman B., William M. Morrow, and Rangaraj M. Rangayyan. "Adaptive-neighborhood histogram equalization for image enhancement." CVGIP: Graphical Models and Image Processing 54, no. 3 (1992): 259–67. http://dx.doi.org/10.1016/1049-9652(92)90056-4.

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Stark, J. A., and W. J. Fitzgerald. "An Alternative Algorithm for Adaptive Histogram Equalization." Graphical Models and Image Processing 58, no. 2 (1996): 180–85. http://dx.doi.org/10.1006/gmip.1996.0015.

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35

Zarie, Majid, Jafar Khalilpour, and Farhad Sadeghi Almaloo. "Implementation of the electronic sector of the satellite camera and image contrast enhancement." Indonesian Journal of Electrical Engineering and Computer Science 20, no. 2 (2020): 690. http://dx.doi.org/10.11591/ijeecs.v20.i2.pp690-703.

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The purpose of this paper is to design and implement the electronic sector of satellite camera on the basis of systematic calculations as well as presenting the general and detailed block diagram. The main parts of the designed camera consist of four units named “optics, detector, processors, and memory” that can work in one of three modes: real-time, storage and storage-send. Verification of the simulation and practical results are shown completely by receiving images. According to the conditions of the satellite imaging, in most cases, there is a need to improve the image quality. A very imp

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Majid, Zarie, Khalilpour Jafar, and Sadeghi Almaloo Farhad. "Implementation of the electronic sector of the satellite camera and image contrast enhancement." Indonesian Journal of Electrical Engineering and Computer Science 20, no. 2 (2020): 690–703. https://doi.org/10.11591/ijeecs.v20.i2.pp690-703.

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The purpose of this paper is to design and implement the electronic sector of satellite camera on the basis of systematic calculations as well as presenting the general and detailed block diagram. The main parts of the designed camera consist of four units named “optics, detector, processors, and memory” that can work in one of three modes: real-time, storage and storagesend. Verification of the simulation and practical results are shown completely by receiving images. According to the conditions of the satellite imaging, in most cases, there is a need to improve the image quality.

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Nurhidayah, Bannu Abdul Samad, and Bualkar Abdullah. "Perbandingan Metode Contrast Enhancement pada Citra CT-Scan Kanker Paru-paru." Gravitasi 19, no. 2 (2020): 24–28. http://dx.doi.org/10.22487/gravitasi.v19i2.15360.

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Abstrak: Di Indonesia kanker paru menjadi penyebab kematian kedua setelah kanker payudara. Angka mortalitas yang cukup tinggi, maka penentuan diagnosis lebih awal memegang peranan yang sangat penting dalam manajemen terapi. Kelemahan CT-Scan dalam mendiagnosa kanker paru-paru disebabkan oleh kontras citra yang rendah dan derau pada citra. Pada penelitian ini akan membandingkan metode contrast enhancement berbasis histogram equalization dan contrast limited adaptive histogram equalization untuk meningkatkan kualitas citra dengan menggunakan software Matlab. Namun, sebelumnya dilakukan red

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Kumari, R. M. L. N., G. A. C. T. Bandara, and Maheshi B. Dissanayake. "Sylvester Matrix-Based Similarity Estimation Method for Automation of Defect Detection in Textile Fabrics." Journal of Sensors 2021 (January 14, 2021): 1–11. http://dx.doi.org/10.1155/2021/6625421.

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Fabric defect detection is a crucial quality control step in the textile manufacturing industry. In this article, a machine vision system based on the Sylvester Matrix-Based Similarity Method (SMBSM) is proposed to automate the defect detection process. The algorithm involves six phases, namely, resolution matching, image enhancement using Histogram Specification and Median–Mean-Based Sub-Image-Clipped Histogram Equalization, image registration through alignment and hysteresis process, image subtraction, edge detection, and fault detection by means of the rank of the Sylvester matrix. The expe

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Manimekalai, M. A. P., and N. A. Vasanthi. "Hybrid Lempel–Ziv–Welch and clipped histogram equalization based medical image compression." Cluster Computing 22, S5 (2018): 12805–16. http://dx.doi.org/10.1007/s10586-018-1761-7.

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Subramani, Bharath, and Magudeeswaran Veluchamy. "Quadrant dynamic clipped histogram equalization with gamma correction for color image enhancement." Color Research & Application 45, no. 4 (2020): 644–55. http://dx.doi.org/10.1002/col.22502.

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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 (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. "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 (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

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Andiani. "Multi-Scale Adaptive Contrast Enhancement (MSACE) for Color Images: A Comparative Analysis with Conventional Techniques." Journal of Information Systems Engineering and Management 10, no. 8s (2025): 49–55. https://doi.org/10.52783/jisem.v10i8s.956.

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This study introduces a novel contrast enhancement algorithm, Multi-Scale Adaptive Con- trast Enhancement (MSACE), which adapts contrast adjustments across multiple brightness scales in color images while preserving fine details. MSACE employs brightness segmenta- tion, adaptive scaling, and edge-based detail preservation to deliver enhanced contrast with- out overexposure or detail loss, addressing limitations found in conventional methods. Com- parative results between MSACE and traditional methods—Simple Contrast Scaling, Gamma Correction, Histogram Equalization, and Adaptive Histogram Equa

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Riadi, Aditya Akbar, Ahmad Abdul Chamid, and Akh Sokhibi. "ANALISIS KOMPARASI METODE PERBAIKAN KONTRAS BERBASIS HISTOGRAM EQUALIZATION PADA CITRA MEDIS." Simetris: Jurnal Teknik Mesin, Elektro dan Ilmu Komputer 8, no. 1 (2017): 383–88. http://dx.doi.org/10.24176/simet.v8i1.1026.

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Citra merupakan gambaran tentang karakteristik suatu obyek menurut kondisi variabel tertentu. Pengolahan citra bertujuan memperbaiki kualitas citra agar mudah diinterpretasi oleh manusia atau mesin (dalam hal ini komputer). Terdapat beberapa operasi di dalam pengolahan citra, salah satunya adalah perbaikan kontras yang pada dasarnya biasa digunakan untuk memunculkan bagian-bagian yang tidak terlihat (hidden feature) pada citra. Hasil citra dari rontgen yang tidak selalu memiliki kualitas citra yang baik, seperti halnya hasil citra x-ray yang terlalu gelap atau ada bagian tulang yang terlihat s

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Gopal, Shefali Srivastava, and Smriti Srivastava. "Biometric authentication using local subspace adaptive histogram equalization." Journal of Intelligent & Fuzzy Systems 32, no. 4 (2017): 2893–99. http://dx.doi.org/10.3233/jifs-169232.

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Acharya, Kuldip, and Dibyendu Ghoshal. "Central Moment and Multinomial Based Sub Image Clipped Histogram Equalization for Image Enhancement." International Journal of Image, Graphics and Signal Processing 13, no. 1 (2021): 1–12. http://dx.doi.org/10.5815/ijigsp.2021.01.01.

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Zarie, Majid, Hassan Hajghassem, and Abdollah Eslami Majd. "Contrast enhancement using triple dynamic clipped histogram equalization based on mean or median." Optik 175 (December 2018): 126–37. http://dx.doi.org/10.1016/j.ijleo.2018.08.082.

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Sari, Ni Larasati Kartika, Maria Oktavianti, and Samsun Samsun. "Analisis Karakter Segmen Abnormal pada Citra Mamografi dengan Menggunakan Berbagai Metode Preprocessing Citra." Jurnal Ilmiah Giga 22, no. 1 (2020): 1. http://dx.doi.org/10.47313/jig.v22i1.737.

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Penelitian ini menganalisis pengaruh penerapan beberapa jenis algoritma preprocessing untuk mencari karakteristik segmen abnormal yang tampak pada citra mamografi. Mamografi merupakan pemeriksaan radiografi khusus payudara. Penerapan algoritma preprocessing yang terdiri dari metode filtering, contrast enhancement, sharpening, dan smoothing diharapkan dapat mengurangi noise dan meningkatkan kontras citra mamografi serta membantu ahli radiologi untuk melakukan diagnosis pada citra. Pada penelitian ini akan digunakan dua algoritma filtering yaitu median filter dan gaussian filter. Selain itu digu

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Zhuang, Liyun, and Yepeng Guan. "Adaptive Image Enhancement Using Entropy-Based Subhistogram Equalization." Computational Intelligence and Neuroscience 2018 (August 13, 2018): 1–13. http://dx.doi.org/10.1155/2018/3837275.

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A novel image enhancement approach called entropy-based adaptive subhistogram equalization (EASHE) is put forward in this paper. The proposed algorithm divides the histogram of input image into four segments based on the entropy value of the histogram, and the dynamic range of each subhistogram is adjusted. A novel algorithm to adjust the probability density function of the gray level is proposed, which can adaptively control the degree of image enhancement. Furthermore, the final contrast-enhanced image is obtained by equalizing each subhistogram independently. The proposed algorithm is compa

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B Shoba Rani. "Modified Effective Histogram Equalization method for Night Time Color image enhancement with Energy Curve." Journal of Information Systems Engineering and Management 10, no. 49s (2025): 873–83. https://doi.org/10.52783/jisem.v10i49s.10001.

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By modifying brightness, contrast, sharpness, and color balance, color photographs can be made more visually appealing. By highlighting significant characteristics and reducing noise or distortion, the main aim of enhancement is to make the image more aesthetically visible, lucid, and interpretable. Techniques vary from basic brightness and contrast tweaks to sophisticated algorithms. Improved quality in low-light video is vital for distinguishing individuals and activities in security and surveillance. Challenges like noise amplification and over-enhancement can create unnatural images with e

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