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Journal articles on the topic 'Histogram Equalization-HE'

Author: Grafiati

Published: 3 June 2025

Last updated: 1 August 2025

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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 (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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3

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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8

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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Fan, Xin, Junyan Wang, Haifeng Wang, and Changgao Xia. "Contrast-Controllable Image Enhancement Based on Limited Histogram." Electronics 11, no. 22 (2022): 3822. http://dx.doi.org/10.3390/electronics11223822.

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To address the technical shortcomings of conventional histogram equalization (HE), such as over-enhancement and artifacts, we propose a histogram-constrained and contrast-tunable HE technique for digital image enhancement. Firstly, the input image histogram is partitioned into two parts, the main histogram and the constrained histogram, by a cumulative probability density threshold; second, the main histogram is redistributed equally in the whole grayscale range; and finally, the nonlinearity of the constrained histogram is mapped to the main histogram. The experimental averages show that the

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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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Saifullah, Shoffan. "ANALISIS PERBANDINGAN HE DAN CLAHE PADA IMAGE ENHANCEMENT DALAM PROSES SEGMENASI CITRA UNTUK DETEKSI FERTILITAS TELUR." Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) 9, no. 1 (2020): 134. http://dx.doi.org/10.23887/janapati.v9i1.23013.

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Perkembangan teknologi di bidang peternakan mampu memberikan kemudahan dalam proses penetasan ayam. Namun, proses deteksi fertilitas telur telah diperiksa secara manual oleh pekerja yang menyortir telur yang fertil dan infertil. Penelitian ini bertujuan untuk mempermudah proses pendeteksian gambar fertilitas telur menggunakan sistem komputerisasi secara otomatis. Deteksi fertilitas telur dilakukan preprocessing dengan metode Image Enhancement. Dalam metode ini, metode Histogram Equalization (HE) dan metode Contrast Limited Adaptive Histogram Equalization (CLAHE) dibandingkan satu sama lain pad

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Román, Julio César Mello, Vicente R. Fretes, Carlos G. Adorno, et al. "Panoramic Dental Radiography Image Enhancement Using Multiscale Mathematical Morphology." Sensors 21, no. 9 (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 artic

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

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<span id="docs-internal-guid-43432eef-7fff-9949-6deb-865191ff0740"><span>Usually, X-ray image has distortion in many parts because it is focusing on bones rather than other, However, when dentist needs to make decision analysis, he does that by using X-ray and many opinions can be judged by looking closely on it like (inflammation, infection, tooth nerve, root of the tooth…). This paper proposes on new suggested technique by applying multilayers of histogram equalization (HE) and contrast limited adaptive histogram equalization (CLAHE) in order to make high contrast of X-ray, this

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P., S. Aithal, and Prasad K. Krishna. "A Novel Tuning Based Contrast Adjustment Algorithm for Grayscale Fingerprint Image." Saudi Journal of Engineering and Technology 3, no. 1 (2018): 15–23. https://doi.org/10.5281/zenodo.1195705.

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In Filtering contrast, brightness and normalization of the image are performed with an ultimate goal to remove or reduce the noise to a maximum extent. Contrast and Brightness are two major factors, which affect the superiority of an image for easy or stainless or pleasant viewing. Equalization through Histogram (HE) is a very famous approach for image contrast adjustment or enhancement in image processing. In general, the histogram equalization distributes pixel values consistently and produces an outcome in a superior image with the linear increasing histogram. Contrast adjustment is the par

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Naufal, Muhammad, Harun Al Azies, Gustian Angga Firmansyah, and Ni Made Kirei Kharisma. "PENERAPAN TEKNIK ADAPTIVE DAN HISTOGRAM EQUALIZATION DALAM PENGOLAHAN CITRA." Jurnal Mahasiswa Ilmu Komputer 5, no. 1 (2024): 9–18. http://dx.doi.org/10.24127/ilmukomputer.v5i1.5345.

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Mengantuk saat berkendara menjadi ancaman serius yang dapat meningkatkan risiko kecelakaan, yang merupakan penyebab utama kematian di seluruh dunia, termasuk di Indonesia. Deteksi dan pencegahan kondisi mengantuk pada tahap awal menjadi krusial untuk mengurangi potensi kecelakaan dan meningkatkan keselamatan berkendara. Penelitian ini fokus pada pemanfaatan citra wajah pengemudi sebagai metode efektif dalam mendeteksi mengantuk. Rendahnya kontras dalam citra dapat mempengaruhi deteksi wajah, sehingga diperlukan peningkatan kontras citra. Dalam penelitian ini, dua teknik peningkatan kontras cit

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Archana B and K. Kalirajan. "Contrast Enhancement of Alzheimer’s MRI using Histogram Analysis." Journal of Innovative Image Processing 5, no. 4 (2023): 379–89. http://dx.doi.org/10.36548/jiip.2023.4.003.

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Contrast enhancement of MRI images frequently needs considerable pre-processing to provide accurate data for disease diagnosis and proper treatment. Enhancing the appearance of medical images becomes a difficult task owing to the uncertainty of the obtained image quality. In this study, Alzheimer’s MRI images are subjected to a contrast enhancement algorithm for easy diagnosis. A noise reduction and contrast enhancement technique for MRI images is discussed in this research. Histogram-based algorithms are used to solve the problems of de-noising and enhancing the contrast of images for identif

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Rao, Gutta Srinivasa, and Atluri Srikrishna. "Image Pixel Contrast Enhancement Using Enhanced Multi Histogram Equalization Method." Ingénierie des systèmes d information 26, no. 1 (2021): 95–101. http://dx.doi.org/10.18280/isi.260110.

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Image Enhancement methods produce various sorts of problems, for example, unnatural impacts, over-improvement, and these downsides become increasingly unmistakable in improving dull Images. Histogram Equalization (HE) method is a straightforward and generally utilized Image contrast enhancement procedure. The fundamental task of HE is it changes the contrast of the Image. To perform this task, different HE techniques have been proposed. These techniques protect the brightness or contrast on the final Image that doesn't have a characteristic look. To overcome the drawbacks of HE, Enhanced Multi

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Han, Yi, Xiangyong Chen, Yi Zhong, et al. "Low-Illumination Road Image Enhancement by Fusing Retinex Theory and Histogram Equalization." Electronics 12, no. 4 (2023): 990. http://dx.doi.org/10.3390/electronics12040990.

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Low-illumination image enhancement can provide more information than the original image in low-light scenarios, e.g., nighttime driving. Traditional deep-learning-based image enhancement algorithms struggle to balance the performance between the overall illumination enhancement and local edge details, due to limitations of time and computational cost. This paper proposes a histogram equalization–multiscale Retinex combination approach (HE-MSR-COM) that aims at solving the blur edge problem of HE and the uncertainty in selecting parameters for image illumination enhancement in MSR. The enhanced

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Saifullah, Shoffan. "Segmentasi Citra Menggunakan Metode Watershed Transform Berdasarkan Image Enhancement Dalam Mendeteksi Embrio Telur." Systemic: Information System and Informatics Journal 5, no. 2 (2020): 53–60. http://dx.doi.org/10.29080/systemic.v5i2.798.

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Image processing dapat diterapkan dalam proses deteksi embrio telur. Proses deteksi embrio telur dilakukan dengan menggunakan proses segmentasi, yang membagi citra sesuai dengan daerah yang dibagi. Proses ini memerlukan perbaikan citra yang diproses untuk memperoleh hasil optimal. Penelitian ini akan menganalisis deteksi embrio telur berdasarkan image processing dengan image enhancement dan konsep segmentasi menggunakan metode watershed transform. Image enhacement pada preprocessing dalam perbaikan citra menggunakan kombinasi metode Contrast Limited Adaptive Histogram Equalization (CLAHE) dan

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Zhuang, Liyun, and Yepeng Guan. "Image Enhancement via Subimage Histogram Equalization Based on Mean and Variance." Computational Intelligence and Neuroscience 2017 (2017): 1–12. http://dx.doi.org/10.1155/2017/6029892.

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This paper puts forward a novel image enhancement method via Mean and Variance based Subimage Histogram Equalization (MVSIHE), which effectively increases the contrast of the input image with brightness and details well preserved compared with some other methods based on histogram equalization (HE). Firstly, the histogram of input image is divided into four segments based on the mean and variance of luminance component, and the histogram bins of each segment are modified and equalized, respectively. Secondly, the result is obtained via the concatenation of the processed subhistograms. Lastly,

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Sirait, Pahala, Albert Albert, Hendri Hendri, Juniardi H, and Hernawati Gohzali. "Kajian Algoritma Peningkatan Kontras Citra Dengan Fast Hue Dan Range Preserving Histogram Equalization Specification." Jurnal SIFO Mikroskil 17, no. 2 (2016): 181–91. http://dx.doi.org/10.55601/jsm.v17i2.335.

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Faktor pencahayaan yang kurang saat suatu citra diakuisisi membuat citra menjadi gelap. Untuk memperbaiki tingkat kecerahan kontras citra, beberapa metode telah dilakukan seperti Fast Hue and Range Preserving Histogram Equalization Specification yang meliputi Algoritma Naik and Murthy, algoritma Optimal Range-Preserving Enhancement, algoritma Multiplicative Color Enhancement dan algoritma Additive Color Enhancement. Pada tahap awal dilakukan proses perataan histogram (Histogram Equalization (HE)). Namun dari beberapa referensi belum dapat ditentukan algoritma yang lebih baik dalam proses penin

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Udayana, I. Putu Agus Eka Darma, I. Made Karang Satria Prawira, and I. Gede Bagus Arya Merta Tika. "Comparison of Artificial Intelligence Methods for Tuberculosis Detection Using X-Ray Images." IJCCS (Indonesian Journal of Computing and Cybernetics Systems) 19, no. 1 (2025): 49. https://doi.org/10.22146/ijccs.102601.

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Penyakit tuberkulosis (TB), yang disebabkan oleh bakteri Mycobacterium tuberculosis, merupakan penyakit menular yang sangat berbahaya. Di Indonesia, TB adalah penyakit menular paling mematikan setelah COVID-19 dan menempati urutan ke-13 sebagai penyebab kematian global. Deteksi dini TB sangat penting untuk meningkatkan peluang kesembuhan, namun keterbatasan jumlah ahli radiologi menjadi tantangan utama. Teknologi deep learning, khususnya Convolutional Neural Network (CNN), mejadi solusi efektif untuk masalah ini. Oleh karena itu, pada penelitian ini akan membandingkan dua arsitektur CNN, yaitu

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Saifullah, Shoffan, Andri Pranolo, and Rafał Dreżewski. "Comparative analysis of image enhancement techniques for braintumor segmentation: contrast, histogram, and hybrid approaches." E3S Web of Conferences 501 (2024): 01020. http://dx.doi.org/10.1051/e3sconf/202450101020.

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This study systematically investigates the impact of image enhancement techniques on Convolutional Neural Network (CNN)-based Brain Tumor Segmentation, focusing on Histogram Equalization (HE), Contrast Limited Adaptive Histogram Equalization (CLAHE), and their hybrid variations. Employing the U-Net architecture on a dataset of 3064 Brain MRI images, the research delves into preprocessing steps, including resizing and enhancement, to optimize segmentation accuracy. A detailed analysis of the CNN-based U-Net architecture, training, and validation processes is provided. The comparative analysis,

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Gupta, Shubhanshi, Ashutosh Gupta, and Gagan Minocha. "Image Enhancement based on Contrast Enhancement & Fuzzification Histogram Equalization and Comparison with Contrast Enhancement Techniques." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 7, no. 2 (2013): 594–99. http://dx.doi.org/10.24297/ijct.v7i2.3461.

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Contrast Enhancement is a technique which comes into the part of Image Enhancement. Contrast Enhancement is used to enhance the visual quality of any captured or other image. Contrast Enhancement can be performed with the help of Histogram equalization (HE). In this technique, the image is collected in the gray scale allocation. The image is then partitioning and applying adaptive Histogram equalization (AHE). Fuzzy logic provides a set of logics which enhance the contrast and visibility of any image. In this technique, the visual quality and the contrast of image will change and then compare

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Firma, Aulia, and Sri Oktamuliani. "PENGOLAHAN FILTERING DAN CONTRAST ENHANCEMENT UNTUK MENINGKATKAN KUALITAS RESOLUSI CITRA ULTRASONOGRAFI ABDOMEN." JOURNAL ONLINE OF PHYSICS 8, no. 1 (2022): 51–54. http://dx.doi.org/10.22437/jop.v8i1.20607.

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Evaluasi penyaringan dan peningkatan kontras telah dilakukan untuk meningkatkan visualisasi gambar USG perut. Penelitian ini bertujuan untuk mengevaluasi hasil gabungan metode median filter dan filter Wiener menggunakan metode Histogram Equalization (HE), Contras Limited Adaptive Histogram Equalization (CLAHE), Contras Stretching (CS) secara kuantitatif menggunakan Mean Squared Error (MSE) dan Peak Signal-to-Noise Ratio (PSNR) dan kualitatif berdasarkan hasil wawancara dengan ahli radiologi. Data yang digunakan dalam penelitian ini adalah 30 data sekunder dari pasien USG abdomen. Penelitian di

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Bataineh, Bilal. "Image contrast enhancement for preserving entropy and image visual features." International Journal of Advances in Intelligent Informatics 9, no. 2 (2023): 161. http://dx.doi.org/10.26555/ijain.v9i2.907.

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Histogram equalization is essential for low-contrast enhancement in image processing. Several methods have been proposed; however, one of the most critical problems encountered by existing methods is their ability to preserve information in the enhanced image as the original. This research proposes an image enhancement method based on a histogram equalization approach that preserves the entropy and fine details similar to those of the original image. This is achieved through proposed probability density functions (PDFs) that preserve the small gray values of the usual PDF. The method consists

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Juslan, Wulandari, and Alva Hendi Muhammad. "Evaluasi Kinerja Metode Peningkatan Kontras (CLAHE & HE) pada Klasifikasi Ras Kucing menggunakan VGG16." Edumatic: Jurnal Pendidikan Informatika 9, no. 1 (2025): 246–55. https://doi.org/10.29408/edumatic.v9i1.29578.

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Cat breed classification is challenging in image processing due to complex visual variations from crossbreeding, which affect care requirements. This study evaluates the effectiveness of Contrast Limited Adaptive Histogram Equalization (CLAHE) and Histogram Equalization (HE) in cat breed classification using a VGG16-based Convolutional Neural Network (CNN). The dataset consists of 4,656 cat images from six breeds, processed with CLAHE and HE for contrast enhancement before training. It is divided into 70% for training, 15% for validation, and 15% for testing. The model is trained for 10 epochs

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Saad, Nor Hidayah, Nor Ashidi Mat Isa, Hariyanti Mohd Saleh, Farah Hanim Mohd Fauzi, and Shivam Gangwar. "Modified Histogram Equalization for Non-uniform Illumination Underwater Image Enhancement." Journal of Advanced Research in Applied Sciences and Engineering Technology 61, no. 3 (2024): 151–62. https://doi.org/10.37934/araset.61.3.151162.

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Non-uniform illumination underwater image occasionally produced during image acquisition. Underwater image has low visibility due to light absorption and dispersion, therefore by using an artificial light source to increase visibility can occasionally result in different illumination regions in the image. In order to generate uniform illumination image and restore the loss details, image enhancement must be performed. Despite the fact that Histogram Equalization (HE) is well known and widely used in image enhancement, current HE-based approaches usually produce washed-out effects and have an u

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Kurmasha, H. T. R., A. F. H. Alharan, C. S. Der, and N. H. Azami. "Enhancement of Edge-based Image Quality Measures Using Entropy for Histogram Equalization-based Contrast Enhancement Techniques." Engineering, Technology & Applied Science Research 7, no. 6 (2017): 2277–81. http://dx.doi.org/10.48084/etasr.1625.

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An Edge-based image quality measure (IQM) technique for the assessment of histogram equalization (HE)-based contrast enhancement techniques has been proposed that outperforms the Absolute Mean Brightness Error (AMBE) and Entropy which are the most commonly used IQMs to evaluate Histogram Equalization based techniques, and also the two prominent fidelity-based IQMs which are Multi-Scale Structural Similarity (MSSIM) and Information Fidelity Criterion-based (IFC) measures. The statistical evaluation results show that the Edge-based IQM, which was designed for detecting noise artifacts distortion

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Kurmasha, H. T. R., A. F. H. Alharan, C. S. Der, and N. H. Azami. "Enhancement of Edge-based Image Quality Measures Using Entropy for Histogram Equalization-based Contrast Enhancement Techniques." Engineering, Technology & Applied Science Research 7, no. 6 (2017): 2277–81. https://doi.org/10.5281/zenodo.1118976.

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An Edge-based image quality measure (IQM) technique for the assessment of histogram equalization (HE)-based contrast enhancement techniques has been proposed that outperforms the Absolute Mean Brightness Error (AMBE) and Entropy which are the most commonly used IQMs to evaluate Histogram Equalization based techniques, and also the two prominent fidelity-based IQMs which are Multi-Scale Structural Similarity (MSSIM) and Information Fidelity Criterion-based (IFC) measures. The statistical evaluation results show that the Edge-based IQM, which was designed for detecting noise artifacts distortion

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Chen, Jiahe, and Yi Li. "Data Pre-processing method of Ground Penetrating Radar based on HE-R2M." Highlights in Science, Engineering and Technology 107 (August 15, 2024): 628–37. http://dx.doi.org/10.54097/am4y7h57.

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Ground Penetrating Radar (GPR), as a non-destructive detection technology, has been widely used in infrastructure construction, tunneling, agriculture and forestry. It has the advantages of fast detection speed, high accuracy and non-destructive detection. However, GPR is often affected by the interference of subsurface media when facing complex geological environments, resulting in low contrast, high noise and clutter interference in the acquired B-scan images, which seriously affects the detection of subsurface structures and imaging performance. In order to improve the quality of images acq

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Chyan, Phie. "Metode Modifikasi Histogram Untuk Peningkatan Kontras dan Kecerahan Citra." JSAI (Journal Scientific and Applied Informatics) 1, no. 3 (2018): 76–80. http://dx.doi.org/10.36085/jsai.v1i3.64.

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Histogram Equalization adalah merupakan metode yang paling sering digunakan untuk meningkatkan kontras pada citra digital. Sebagai hasilnya citra yang diproses menggunakan metode HE memiliki efek negatif seperti tampilan yang kelihatan buram dan kontur yang berubah akibat perubahan pada kecerahan gambar. Untuk mengatasi masalah tersebut diperlukan model HE yang dapat memelihara tingkat kecerahan citra. Umumnya, metode tersebut mempartisi histogram dari citra asli ke dalam sub histogram dan kemudian secara independen melakukan ekualisasi terhadap sub histogram tersebut. Penelitian ini menghasil

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Wahyono. "Analisis Pengaruh Image Enhancement Pada Pendeteksian COVID-19 Berbasis Citra X-Ray." Techno.Com 22, no. 1 (2023): 186–94. http://dx.doi.org/10.33633/tc.v22i1.7195.

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Penyakit virus corona 2019 (COVID-19) merupakan keadaan darurat kesehatan masyarakat secara global. Salah satu cara untuk dapat mendeteksi adanya COVID-19 adalah dengan memanfaatkan citra x-ray dada yang mengidentifikasi anomali pada area paru-paru. Namun terkadang citra yang didapatkan pada melalui scan x-ray memiliki kualitas yang buruk sehingga sulit secara langsung untuk bisa dianalisis secara manual atau menggunakan model machine learning. Untuk menghasilkan analisis yang lebih baik, biasanya citra akan ditingkatkan terlebih dahulu kualitasnya dengan teknik image enhancement. Banyak metod

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Su, Xiongfei, Siyuan Li, Yuning Cui, et al. "Prior-guided Hierarchical Harmonization Network for Efficient Image Dehazing." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 7 (2025): 7042–50. https://doi.org/10.1609/aaai.v39i7.32756.

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Image dehazing is a crucial task that involves the enhancement of degraded images to recover their sharpness and textures. While vision Transformers have exhibited impressive results in diverse dehazing tasks, their quadratic complexity and lack of dehazing priors pose significant drawbacks for real-world applications. In this paper, guided by triple priors, Bright Channel Prior (BCP), Dark Channel Prior (DCP), and Histogram Equalization (HE), we propose a Prior-guided Hierarchical Harmonization Network (PGHHNet) for image dehazing. PGHNet is built upon the UNet-like architecture with an effic

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Łabędź, Piotr, Krzysztof Skabek, Paweł Ozimek, and Mateusz Nytko. "Histogram Adjustment of Images for Improving Photogrammetric Reconstruction." Sensors 21, no. 14 (2021): 4654. http://dx.doi.org/10.3390/s21144654.

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The accuracy of photogrammetric reconstruction depends largely on the acquisition conditions and on the quality of input photographs. This paper proposes methods of improving raster images that increase photogrammetric reconstruction accuracy. These methods are based on modifying color image histograms. Special emphasis was placed on the selection of channels of the RGB and CIE L*a*b* color models for further improvement of the reconstruction process. A methodology was proposed for assessing the quality of reconstruction based on premade reference models using positional statistics. The analys

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Wu, Zhaoqi, Reziwanguli Xiamixiding, Atul Sajjanhar, Juan Chen, and Quan Wen. "Image Appearance-Based Facial Expression Recognition." International Journal of Image and Graphics 18, no. 02 (2018): 1850012. http://dx.doi.org/10.1142/s0219467818500122.

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We investigate facial expression recognition (FER) based on image appearance. FER is performed using state-of-the-art classification approaches. Different approaches to preprocess face images are investigated. First, region-of-interest (ROI) images are obtained by extracting the facial ROI from raw images. FER of ROI images is used as the benchmark and compared with the FER of difference images. Difference images are obtained by computing the difference between the ROI images of neutral and peak facial expressions. FER is also evaluated for images which are obtained by applying the Local binar

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Nasir, Ahmad Lutfi Afifi Mohd, Roslan Umar, Wan Nural Jawahir Wan Yussof, et al. "Comparative Analysis of Image Processing Technique in Determining the New Crescent Moon Visibility." Journal of Physics: Conference Series 2915, no. 1 (2024): 012004. https://doi.org/10.1088/1742-6596/2915/1/012004.

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Abstract This research presents a comparative analysis of advanced image processing techniques to enhance the visibility of the new crescent moon, a crucial element in astronomy and the lunar calendar. The primary objective is to assess the effectiveness of Contrast Adjustment (CA), Histogram Equalization (HE), Contrast Limited Adaptive Histogram Equalization (CLAHE), and Gamma Correction (GC) in improving new crescent moon visibility. The study utilized a comprehensive dataset of new crescent moon images captured on various dates and times, with each image undergoing a specific image processi

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Saifullah, Shoffan, and Rafał Dreżewski. "Advanced Medical Image Segmentation Enhancement: A Particle-Swarm-Optimization-Based Histogram Equalization Approach." Applied Sciences 14, no. 2 (2024): 923. http://dx.doi.org/10.3390/app14020923.

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Accurate medical image segmentation is paramount for precise diagnosis and treatment in modern healthcare. This research presents a comprehensive study of the efficacy of particle swarm optimization (PSO) combined with histogram equalization (HE) preprocessing for medical image segmentation, focusing on lung CT scan and chest X-ray datasets. Best-cost values reveal the PSO algorithm’s performance, with HE preprocessing demonstrating significant stabilization and enhanced convergence, particularly for complex lung CT scan images. Evaluation metrics, including accuracy, precision, recall, F1-sco

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Ao, Jun, and Chunbo Ma. "Adaptive Stretching Method for Underwater Image Color Correction." International Journal of Pattern Recognition and Artificial Intelligence 32, no. 02 (2017): 1854001. http://dx.doi.org/10.1142/s0218001418540010.

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The physical properties of water lead to attenuation of light that travels through the water channel. The attenuation is dependent on the color spectrum wavelength, that results in low contrast and color cast in image acquisition. Several methods have been proposed to handle these problems, such as Linear Stretching, Histogram Equalization (HE) and their variants. Considering the advantages of HE and Linear Stretching, this paper presents a new Adaptive Linear Stretch method (ALS) which can efficiently improve the subjective impression of the traditional Linear Stretching and keep the computat

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Sarif, Akhmad, and Dadang Gunawan. "Perbandingan Metode Penyesuaian Kontras Citra Pada Pengenalan Ekspresi Wajah Menggunakan Fine-Tuning AlexNet." JURNAL MEDIA INFORMATIKA BUDIDARMA 7, no. 3 (2023): 1144. http://dx.doi.org/10.30865/mib.v7i3.6382.

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Research related to facial expression recognition (FER) has become a significant topic of interest in the field of computer vision due to its broad applications. Artificial intelligence technologies, such as deep learning, have been applied in FER research. The use of deep learning models in FER requires a dataset for training, which plays a crucial role in determining the performance of deep learning. However, the available FER datasets often require preprocessing before being processed using deep learning. In this study, a comparison of contrast adjustment preprocessing methods was conducted

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Riadi, Aditya Akbar. "ANALISA PERBAIKAN KUALITAS KONTRAS CITRA X-RAY MENGGUNAKAN METODE EXPOSURE BASED SUB-IMAGE HISTOGRAMEQUALIZATION (ESIHE)." Simetris : Jurnal Teknik Mesin, Elektro dan Ilmu Komputer 7, no. 1 (2016): 305. http://dx.doi.org/10.24176/simet.v7i1.519.

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Perbaikan kualitas citra adalah proses dimana kualitas visual dari suatu citra ditingkatan sehingga didapatkan hasil yang lebih baik dari citra yang asli atau citra awal. Proses perbaikan kualitas citra dibagi menjadi dua domain yaitu domain frekuensi dan domain spasial. Dalam domain frekuensi, teknik beroperasi pada pemilihan frekuensi yang akan difilter. Sedangkan di domain spasial, teknik beroperasi secara langsung pada piksel citra. Pencahayaan berdasarkan gambar asli dan pemerataan histogram sub gambar terbukti sebagai teknik yang sangat efektif untuk meningkatkan pencahayaan yang kurang.

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Kurniawan, Muhammad Bayu, and Ema Utami. "COMPARATIVE ANALYSIS OF CONTRAST ENHANCEMENT METHODS FOR CLASSIFICATION OF PEKALONGAN BATIK MOTIFS USING CONVOLUTIONAL NEURAL NETWORK." Jurnal Teknik Informatika (Jutif) 5, no. 6 (2024): 1779–87. https://doi.org/10.52436/1.jutif.2024.5.6.2621.

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Batik artists in Pekalongan have freedom in determining motifs, creating a diversity of distinctive batik motifs. However, this diversity often makes it difficult for people to recognize the different motifs, as visual identification requires in-depth knowledge. The lack of understanding about Pekalongan batik is a challenge in recognizing these motifs. To overcome this challenge, an efficient and accurate method of motif identification is needed. This study aims to analyze the efficacy of contrast enhancement methods in improving the classification results of Pekalongan batik motifs using con

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Peng, Yan-Tsung, Yen-Rong Chen, Zihao Chen, Jung-Hua Wang, and Shih-Chia Huang. "Underwater Image Enhancement Based on Histogram-Equalization Approximation Using Physics-Based Dichromatic Modeling." Sensors 22, no. 6 (2022): 2168. http://dx.doi.org/10.3390/s22062168.

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This work proposes to develop an underwater image enhancement method based on histogram-equalization (HE) approximation using physics-based dichromatic modeling (PDM). Images captured underwater usually suffer from low contrast and color distortions due to light scattering and attenuation. The PDM describes the image formation process, which can be used to restore nature-degraded images, such as underwater images. However, it does not assure that the restored images have good contrast. Thus, we propose approximating the conventional HE based on the PDM to recover the color distortions of under

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Attia, Salim J. "Assessment of Some Enhancement Methods of Renal X-ray Image." NeuroQuantology 18, no. 12 (2020): 01–05. http://dx.doi.org/10.14704/nq.2020.18.12.nq20231.

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The study focuses on assessment of the quality of some image enhancement methods which were implemented on renal X-ray images. The enhancement methods included Imadjust, Histogram Equalization (HE) and Contrast Limited Adaptive Histogram Equalization (CLAHE). The images qualities were calculated to compare input images with output images from these three enhancement techniques. An eight renal x-ray images are collected to perform these methods. Generally, the x-ray images are lack of contrast and low in radiation dosage. This lack of image quality can be amended by enhancement process. Three q

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Naraloka, Therezia, Lucky Indra Kesuma, Ade Sukmawati, and Marissa Cristianti. "Arsitektur U-Net pada Segmentasi Citra Hati sebagai Deteksi Dini Kanker Liver." Techno.Com 21, no. 4 (2022): 753–64. http://dx.doi.org/10.33633/tc.v21i4.6669.

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Hati adalah salah satu organ tubuh manusia yang bertanggung jawab untuk mencerna, meyerap, dan memproses makanan serta berfungsi untuk menyaring darah dari saluran pencernaan sebelum dibawa kebagian organ tubuh lainnya. Hati sangat rentan terhadap berbagai penyakit, salah satunya yaitu kanker liver. untuk itu perlu dilakukannya deteksi sejak dini atau diagnosa terhadap organ hati. Untuk mengatasi permasalahan tersebut, pada penelitian ini dilakukan segmentasi hati menggunakan metode Convolutional Neural Network (CNN) dengan arsitektur U-Net pada citra hati. Langkah awal pada penelitian ini dil

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Loorutu, Nalson Mark, Haniza Yazid, and Khairul Shakir Ab Rahman. "Prostate Cancer Classification Based on Histopathological Images." International Journal on Robotics, Automation and Sciences 5, no. 2 (2023): 43–53. http://dx.doi.org/10.33093/ijoras.2023.5.2.5.

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Prostate cancer is a significant health concern, ranking as the third most common cancer in Malaysian men, with increasing incidence in Asia. The importance of automating the prostate cancer classification process lies in its potential to significantly improve diagnostic accuracy, reduce subjectivity, and enhance overall efficiency compared to the manual approach. The objective of this thesis is two-fold: firstly, to effectively enhance and segment crucial features in the images to aid in the classification process, and secondly, to implement a binary classification task that indicates the pre

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Hamzah, Fahmi Aliefuddin, and Retno Wahyusari. "Perbandingan Perbaikan Citra Magnetic Resonance Imaging (MRI) Menggunakan Ruang Warna RGB, HSV dan YCbCr Dengan Metode Histogram Equalization dan Contrast Streching." JIIFKOM (Jurnal Ilmiah Informatika dan Komputer) 2, no. 2 (2023): 22–26. http://dx.doi.org/10.51901/jiifkom.v2i2.355.

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Gambar Magnetic Resonance Imaging (MRI) merupakan gambar yang paling sering digunakan dalam bidang radiologi. Beberapa masalah yang sering terjadi pada citra medis adalah hasil scan yang mengalami penurunan kualitas karena faktor noise. Citra MRI yang telah dicetak kemudian masuk ke sistem komputerisasi akan mengalami penurunan kualitas seperti citra terlihat buram atau gelap. Sehingga diperlukan peningkatan kualitas citra untuk menciptakan citra yang berkualitas agar memudahkan dokter dalam mendiagnosa dan mengurangi kemungkinan kesalahan analisis. Teknik peningkatan citra (IE) banyak diterap

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Oleiwi, Bashra Kadhim, Layla H. Abood, and Maad Issa Al Tameemi. "Human visualization system based intensive contrast improvement of the collected COVID-19 images." Indonesian Journal of Electrical Engineering and Computer Science 27, no. 3 (2022): 1502–8. https://doi.org/10.11591/ijeecs.v27.i3.pp1502-1508.

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Enhancement and color correction of images play an important role and can be considered as one of the fundamental and basic operations in image analysis for the purpose of speeding up the diagnosis of the medical images. Improving the quality and contrast of the medical image is the basic requirement for clinicians for obtaining an accurate and accurate medical diagnosis. Thus, getting a clear X-ray image reduces the effort and timewasting. In this study a new idea will be applied for improving image contrast of the collected COVID-19 X-ray images, this idea is based on using Wiener filter, mu

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