Relevant bibliographies by topics / Histogram Equalization-HE

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Academic literature on the topic 'Histogram Equalization-HE'

Author: Grafiati
Published: 3 June 2025
Last updated: 1 August 2025

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Journal articles on the topic "Histogram Equalization-HE"

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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 (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 exis
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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
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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 (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 l
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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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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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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 (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
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Farah, F. Alkhalid, Mudher Hasan Ahmed, and A. Alhamady Ahmed. "Improving radiographic image contrast using multi layers of histogram equalization technique." International Journal of Artificial Intelligence (IJ-AI) 10, no. 1 (2021): 151–56. https://doi.org/10.11591/ijai.v10.i1.pp151-156.

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Usually, X-ray image has distortion in many parts because it is focusing on bones rather than other, However, when dentist needs to make decision analysis, he does that by using X-ray and many opinions can be judged by looking closely on it like (inflammation, infection, tooth nerve, root of the tooth…). This paper proposes on new suggested technique by applying multilayers of histogram equalization (HE) and contrast limited adaptive histogram equalization (CLAHE) in order to make high contrast of X-ray, this technique provides very satisfied results and smooth intensity which leads to
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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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Kenyta, Claudia, and Daniel Martomanggolo Wonohadidjojo. "Perbandingan Performa Histogram Equalization untuk Peningkatan Kualitas Gambar Minim Cahaya pada Android." Ultimatics : Jurnal Teknik Informatika 12, no. 2 (2020): 80–88. http://dx.doi.org/10.31937/ti.v12i2.1667.

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When the photos are taken in low light condition, the quality of the results will not meet their expectation. Image Enhancement method can be used to enhance the quality of the photos taken in low light condition. One of the algorithms used is called Histogram Equalization (HE), that works using Histogram basis. The superiority of HE algorithm in enhancing the quality of the photos taken in low light condition is the simplicity of the algorithm itself and it does not need a high specification device for the algorithm to run. One variant of HE algorithm is Contrast Limited Adaptive Histogram Eq
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Sri Arsa, Dewa Made, Grafika Jati, Agung Santoso, Rafli Filano, Nurul Hanifah, and Muhammad Febrian Rachmadi. "COMPARISON OF IMAGE ENHANCEMENT METHODS FOR CHROMOSOME KARYOTYPE IMAGE ENHANCEMENT." Jurnal Ilmu Komputer dan Informasi 10, no. 1 (2017): 50. http://dx.doi.org/10.21609/jiki.v10i1.445.

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The chromosome is a set of DNA structure that carry information about our life. The information can be obtained through Karyotyping. The process requires a clear image so the chromosome can be evaluate well. Preprocessing have to be done on chromosome images that is image enhancement. The process starts with image background removing. The image will be cleaned background color. The next step is image enhancement. This paper compares several methods for image enhancement. We evaluate some method in image enhancement like Histogram Equalization (HE), Contrast-limiting Adaptive Histogram Equaliza
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Book chapters on the topic "Histogram Equalization-HE"

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Dhal, Krishna Gopal, Mandira Sen, Swarnajit Ray, and Sanjoy Das. "Multi-Thresholded Histogram Equalization Based on Parameterless Artificial Bee Colony." In Incorporating Nature-Inspired Paradigms in Computational Applications. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-5020-4.ch004.

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This chapter presents a novel variant of histogram equalization (HE) method called multi-thresholded histogram equalization (MTHE), depending on entropy-based multi-level thresholding-based segmentation. It is reported that proper segmentation of the histogram significantly assists the HE variants to maintain the original brightness of the image, which is one of the main criterion of the consumer electronics field. Multi-separation-based HE variants are also very effective for multi-modal histogram-based images. But, proper multi-seaparation of the histogram increases the computational time of
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Kuan, Ta-Wen, Siying Cai, Xiaodong Yu, et al. "Improved AH2E2 Contrast Enhancement for Deep Learning on Night-Scenario Canal Segmentation." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2025. https://doi.org/10.3233/faia250115.

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The objective of canal semantic segmentation in this work preliminarily investigates the visual intelligence on an SSSB (Self-Sailing Sweeper Boat) that can visualize constraint sailing in a restricted region further for sweeping the canal. The contribution and novelty of this work firstly proposes the night-scenario canal segmentation in terms of the low-lightness problems, in which the boundaries between the canal region and the bank shore cannot be precisely distinguished for ground-true labeling prior to the dataset training and validation, further decreasing the segmentation efficiency. T
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Conference papers on the topic "Histogram Equalization-HE"

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Fang, Yinghong, Guangcun Shan, Tian Wang, Xin Li, Wenliang Liu, and Hichem Snoussi. "HE-SLAM: A Stereo SLAM System Based on Histogram Equalization and ORB Features." In 2018 Chinese Automation Congress (CAC). IEEE, 2018. http://dx.doi.org/10.1109/cac.2018.8623424.

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