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Journal articles on the topic 'Brightness preserving fuzzy histogram equalization'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

Sheet, Debdoot, Hrushikesh Garud, Amit Suveer, Manjunatha Mahadevappa, and Jyotirmoy Chatterjee. "Brightness preserving dynamic fuzzy histogram equalization." IEEE Transactions on Consumer Electronics 56, no. 4 (2010): 2475–80. http://dx.doi.org/10.1109/tce.2010.5681130.

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Magudeeswaran, V., and C. G. Ravichandran. "Fuzzy Logic-Based Histogram Equalization for Image Contrast Enhancement." Mathematical Problems in Engineering 2013 (2013): 1–10. http://dx.doi.org/10.1155/2013/891864.

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Fuzzy logic-based histogram equalization (FHE) is proposed for image contrast enhancement. The FHE consists of two stages. First, fuzzy histogram is computed based on fuzzy set theory to handle the inexactness of gray level values in a better way compared to classical crisp histograms. In the second stage, the fuzzy histogram is divided into two subhistograms based on the median value of the original image and then equalizes them independently to preserve image brightness. The qualitative and quantitative analyses of proposed FHE algorithm are evaluated using two well-known parameters like ave

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3

Subramani, Bharath, and Magudeeswaran Veluchamy. "MRI brain image enhancement using brightness preserving adaptive fuzzy histogram equalization." International Journal of Imaging Systems and Technology 28, no. 3 (2018): 217–22. http://dx.doi.org/10.1002/ima.22272.

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Sreya, John, and Leena Rose Peter Joseph Arul. "Preprocessing of leaf images using brightness preserving dynamic fuzzy histogram equalization technique." International Journal of Artificial Intelligence (IJ-AI) 12, no. 3 (2023): 1149–57. https://doi.org/10.11591/ijai.v12.i3.pp1149-1157.

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Agriculture serves as the backbone of many countries. It provides food and other essential materials as per our requirement. Various kinds of diseases are affecting the agricultural crops which in turn reduce the quantity and quality of the agricultural sector. This can also lead to the decrease in food production thereby affecting the economic growth and development. Even though the symptoms and other impacts of the diseases are outwardly visible, manual identification of diseases and rectification is a tedious and time-consuming process. Therefore, detecting the diseases using an automatic c

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John, Sreya, and Arul Leena Rose Peter Joseph. "Preprocessing of leaf images using brightness preserving dynamic fuzzy histogram equalization technique." IAES International Journal of Artificial Intelligence (IJ-AI) 12, no. 3 (2023): 1149. http://dx.doi.org/10.11591/ijai.v12.i3.pp1149-1157.

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Agriculture serves as the backbone of many countries. It provides food and other essential materials as per our requirement. Various kinds of diseases are affecting the agricultural crops which in turn reduce the quantity and quality of the agricultural sector. This can also lead to the decrease in food production thereby affecting the economic growth and development. Even though the symptoms and other impacts of the diseases are outwardly visible, manual identification of diseases and rectification is a tedious and time-consuming process. Therefore, detecting the diseases using an automatic c

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6

Magudeeswaran, V., C. G. Ravichandran, and P. Thirumurugan. "Brightness preserving bi-level fuzzy histogram equalization for MRI brain image contrast enhancement." International Journal of Imaging Systems and Technology 27, no. 2 (2017): 153–61. http://dx.doi.org/10.1002/ima.22219.

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7

Showkat, Er Eyenan, and Dr Gurinder Kaur Sodhi. "Image enhancement in wavelet domain using histogram equalization and median filters." International Journal of Innovative Research in Computer Science & Technology 11, no. 1 (2023): 25–31. http://dx.doi.org/10.55524/ijircst.2023.11.1.6.

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Image enhancement is one of the challenging but crucial methods that is employed in image processing technology for enhancing the visual appearance of images. This paper presents an effective and efficient image enhancement model in which Non Local mean (NLM) filter is used along with Minimum Mean Brightness Error Bi-Histogram Equalization (MMBEBHE) and Brightness Preserving Dynamic Fuzzy Histogram Equalization (BPDFHE) techniques. The primary objective of the proposed image enhancement model is to enhance the quality of images by reducing the noise effect in them. To do so, we have selected f

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Er., Eyenan Showkat, and Gurinder Kaur Sodhi Dr. "Image enhancement in wavelet domain using histogram equalization and median filters." International Journal of Innovative Research in Computer Science & Technology (IJIRCST) 11, no. 1 (2023): 25–31. https://doi.org/10.5281/zenodo.7660937.

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Image enhancement is one of the challenging but crucial methods that is employed in image processing technology for enhancing the visual appearance of images. This paper presents an effective and efficient image enhancement model in which Non Local mean (NLM) filter is used along with Minimum Mean Brightness Error Bi-Histogram Equalization (MMBEBHE) and Brightness Preserving Dynamic Fuzzy Histogram Equalization (BPDFHE) techniques. The primary objective of the proposed image enhancement model is to enhance the quality of images by reducing the noise effect in them. To do so, we have selected f

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Hajri, Sarra, Fathi Kallel, Ahmed Ben Hamida, and Amine Nait-Ali. "Finger-knuckle-print image enhancement based on brightness preserving dynamic fuzzy histogram equalization and filtering process." Journal of Electronic Imaging 27, no. 03 (2018): 1. http://dx.doi.org/10.1117/1.jei.27.3.033035.

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10

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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Abhishek, Maity, Dasgupta Sayan, and Paul Debjit. "A Novel Approach to Face Detection using Image Parsing and Morphological Analysis." International Journal of Computer Trends and Technology 23, no. 4 (2015): 156–61. https://doi.org/10.14445/22312803/IJCTT-V23P132.

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The main objective of the paper is to propose an algorithm on extraction of some fundamental information of an image efficiently and then finally use that to detect the human face on the image.The method proposed is based on assumption that the image contains the frontal face.The described method widely-used edge-based features. Firstly, the skin region is detected using a colour based algorithm and Brightness preserving histogram equalization techniques operated on RGB to determine the presence of human and the head region is calculated.Lastly, probable calculated head region is extracted usi

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12

Kavitha, R., D. Kiruba Jothi, K. Saravanan, et al. "Ant Colony Optimization-Enabled CNN Deep Learning Technique for Accurate Detection of Cervical Cancer." BioMed Research International 2023 (February 21, 2023): 1–9. http://dx.doi.org/10.1155/2023/1742891.

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Cancer is characterized by abnormal cell growth and proliferation, which are both diagnostic indicators of the disease. When cancerous cells enter one organ, there is a risk that they may spread to adjacent tissues and eventually to other organs. Cancer of the cervix of the uterus often initially manifests itself in the uterine cervix, which is located at the very bottom of the uterus. Both the growth and death of cervical cells are characteristic features of this condition. False-negative results provide a significant moral dilemma since they may cause women to get an incorrect diagnosis of c

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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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14

Sengee, Nyamlkhagva, and Heung Kook Choi. "Brightness preserving weight clustering histogram equalization." IEEE Transactions on Consumer Electronics 54, no. 3 (2008): 1329–37. http://dx.doi.org/10.1109/tce.2008.4637624.

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15

Rajavel, P. "Image dependent brightness preserving histogram equalization." IEEE Transactions on Consumer Electronics 56, no. 2 (2010): 756–63. http://dx.doi.org/10.1109/tce.2010.5505998.

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16

WU, Ying. "Improved brightness preserving bi-histogram equalization algorithm." Journal of Computer Applications 30, no. 6 (2010): 1632–34. http://dx.doi.org/10.3724/sp.j.1087.2010.01632.

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17

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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18

Kuber, Mahendra PS, Manish Dixit, and Sanjay Silakari. "Improving Brightness Using Dynamic Fuzzy Histogram Equalization." International Journal of Signal Processing, Image Processing and Pattern Recognition 8, no. 2 (2015): 303–12. http://dx.doi.org/10.14257/ijsip.2015.8.2.29.

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19

Yeong-Taeg Kim. "Contrast enhancement using brightness preserving bi-histogram equalization." IEEE Transactions on Consumer Electronics 43, no. 1 (1997): 1–8. http://dx.doi.org/10.1109/30.580378.

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20

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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21

Ibrahim, Haidi, and Nicholas Pik Kong. "Brightness Preserving Dynamic Histogram Equalization for Image Contrast Enhancement." IEEE Transactions on Consumer Electronics 53, no. 4 (2007): 1752–58. http://dx.doi.org/10.1109/tce.2007.4429280.

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22

Kong, Nicholas, and Haidi Ibrahim. "Color image enhancement using brightness preserving dynamic histogram equalization." IEEE Transactions on Consumer Electronics 54, no. 4 (2008): 1962–68. http://dx.doi.org/10.1109/tce.2008.4711259.

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23

Chen, Soong-Der, and Abd Rahman Ramli. "Preserving brightness in histogram equalization based contrast enhancement techniques." Digital Signal Processing 14, no. 5 (2004): 413–28. http://dx.doi.org/10.1016/j.dsp.2004.04.001.

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24

Thomas, G., A. Manickavasagan, L. Khriji, and R. Al-Yahyai. "Contrast Enhancement Using Brightness Preserving Histogram Equalization Technique for Classification of Date Varieties." Journal of Engineering Research [TJER] 10, no. 2 (2014): 55. http://dx.doi.org/10.24200/tjer.vol11iss1pp55-63.

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Computer vision technique is becoming popular for quality assessment of many products in food industries. Image enhancement is the first step in analyzing the images in order to obtain detailed information for the determination of quality. In this study, Brightness preserving histogram equalization technique was used to enhance the features of gray scale images to classify three date varieties (Khalas, Fard and Madina). Mean, entropy, kurtosis and skewness features were extracted from the original and enhanced images. Mean and entropy from original images and kurtosis from the enhanced images

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25

Rao, Dr M. Purna Chandra. "Enhancing Visibility Through Image Defogging." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem29912.

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This project introduces a method for enhancing visibility in foggy images through histogram equalization. By leveraging the LAB color space, the method separates luminance and chrominance components, applying histogram equalization solely to the luminance channel. This process effectively enhances contrast and brightness while preserving color fidelity. Experimental results demonstrate significant visibility improvements in foggy scenes, with computational efficiency suitable for real-time applications. This approach holds promise for enhancing visibility in critical domains like transportatio

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26

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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27

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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28

Kawakami, Takashi, Kota Murahira, and Akira Taguchi. "Histogram Equalization with Variable Enhancement Degree for Preserving Mean Brightness." IEEJ Transactions on Electronics, Information and Systems 130, no. 9 (2010): 1687–88. http://dx.doi.org/10.1541/ieejeiss.130.1687.

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29

XU Hong-lie, 许轰烈, 匡. 程. KUANG Cheng, 张. 乐. ZHANG Le, et al. "Range limited adaptive brightness preserving multi-threshold histogram equalization algorithm." Chinese Optics 10, no. 6 (2017): 726–36. http://dx.doi.org/10.3788/co.20171006.0726.

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30

Chao Wang and Zhongfu Ye. "Brightness preserving histogram equalization with maximum entropy: a variational perspective." IEEE Transactions on Consumer Electronics 51, no. 4 (2005): 1326–34. http://dx.doi.org/10.1109/tce.2005.1561863.

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31

Menotti, David, Laurent Najman, Jacques Facon, and Arnaldo A. Araujo. "Multi-Histogram Equalization Methods for Contrast Enhancement and Brightness Preserving." IEEE Transactions on Consumer Electronics 53, no. 3 (2007): 1186–94. http://dx.doi.org/10.1109/tce.2007.4341603.

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32

Khan, Mohammad Farhan, Ekram Khan, and Z. A. Abbasi. "Segment selective dynamic histogram equalization for brightness preserving contrast enhancement of images." Optik 125, no. 3 (2014): 1385–89. http://dx.doi.org/10.1016/j.ijleo.2013.08.005.

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33

Montazeri, Mitra. "Memetic Algorithm Image Enhancement for Preserving Mean Brightness Without Losing Image Features." International Journal of Image and Graphics 19, no. 04 (2019): 1950020. http://dx.doi.org/10.1142/s0219467819500207.

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In the image processing application, contrast enhancement is a major step. Conventional contrast enhancement methods such as Histogram Equalization (HE) do not have satisfactory results on many different low contrast images and they also cannot automatically handle different images. These problems result in specifying parameters manually to produce high contrast images. In this paper, an automatic image contrast enhancement on Memetic algorithm (MA) is proposed. In this study, simple exploiter is proposed to improve the current image contrast. The proposed method accomplishes multi goals of pr

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34

Mehdi, Al, and M. Shahidur. "A Comparative Study between Brightness Preserving Bi-histogram and Tri-histogram Equalization for Image Contrast Enhancement." International Journal of Computer Applications 140, no. 2 (2016): 35–39. http://dx.doi.org/10.5120/ijca2016909233.

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35

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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36

KumarBarode, Mukesh, Rajesh Kumar Rai, and Sachin Murarka. "DWT Curvet based Dynamic Histogram Equalization for Brightness Preserving Contrast Enhancement of Images." International Journal of Computer Applications 110, no. 13 (2015): 32–36. http://dx.doi.org/10.5120/19380-1086.

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37

Cao, Qingjie, Zaifeng Shi, Rong Wang, Pumeng Wang, and Suying Yao. "A brightness-preserving two-dimensional histogram equalization method based on two-level segmentation." Multimedia Tools and Applications 79, no. 37-38 (2020): 27091–114. http://dx.doi.org/10.1007/s11042-020-09265-y.

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38

Fu, Qingqing, Zhengbing Zhang, Mehmet Celenk, and Aiping Wu. "A POSHE-Based Optimum Clip-Limit Contrast Enhancement Method for Ultrasonic Logging Images." Sensors 18, no. 11 (2018): 3954. http://dx.doi.org/10.3390/s18113954.

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Enabled by piezoceramic transducers, ultrasonic logging images often suffer from low contrast and indistinct local details, which makes it difficult to analyze and interpret geologic features in the images. In this work, we propose a novel partially overlapped sub-block histogram-equalization (POSHE)-based optimum clip-limit contrast enhancement (POSHEOC) method to highlight the local details hidden in ultrasonic well logging images obtained through piezoceramic transducers. The proposed algorithm introduces the idea of contrast-limited enhancement to modify the cumulative distribution functio

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39

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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41

Dahmane, Oussama, Mustapha Khelifi, Mohammed Beladgham, and Ibrahim Kadri. "Pneumonia detection based on transfer learning and a combination of VGG19 and a CNN Built from scratch." Indonesian Journal of Electrical Engineering and Computer Science 24, no. 3 (2021): 1469. http://dx.doi.org/10.11591/ijeecs.v24.i3.pp1469-1480.

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In this paper, to categorize and detect pneumonia from a collection of chest X-ray picture samples, we propose a deep learning technique based on object detection, convolutional neural networks, and transfer learning. The proposed model is a combination of the pre-trained model (VGG19) and our designed architecture. The Guangzhou Women and Children's Medical Center in Guangzhou, China provided the chest X-ray dataset used in this study. There are 5,000 samples in the data set, with 1,583 healthy samples and 4,273 pneumonia samples. Preprocessing techniques such as contrast limited adaptive his

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42

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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43

Dahmane, Oussama, Mustapha Khelifi, Mohammed Beladgham, and Ibrahim Kadri. "Pneumonia detection based on transfer learning and a combination of VGG19 and a CNN built from scratch." Indonesian Journal of Electrical Engineering and Computer Science 24, no. 3 (2021): 1469–80. https://doi.org/10.11591/ijeecs.v24.i3.pp1469-1480.

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In this paper, to categorize and detect pneumonia from a collection of chest X-ray picture samples, we propose a deep learning technique based on object detection, convolutional neural networks, and transfer learning. The proposed model is a combination of the pre-trained model (VGG19) and our designed architecture. The Guangzhou Women and Children's Medical Center in Guangzhou, China provided the chest X-ray dataset used in this study. There are 5,000 samples in the data set, with 1,583 healthy samples and 4,273 pneumonia samples. Preprocessing techniques such as contrast limited adaptive

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44

A, Raju, Dwarakish G. S, and D. Venkat Reddy. "A Comparative Analysis of Histogram Equalization based Techniques for Contrast Enhancement and Brightness Preserving." International Journal of Signal Processing, Image Processing and Pattern Recognition 6, no. 5 (2013): 353–66. http://dx.doi.org/10.14257/ijsip.2013.6.5.31.

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45

Kandhway, Pankaj, and Ashish Kumar Bhandari. "An optimal adaptive thresholding based sub-histogram equalization for brightness preserving image contrast enhancement." Multidimensional Systems and Signal Processing 30, no. 4 (2019): 1859–94. http://dx.doi.org/10.1007/s11045-019-00633-y.

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46

Mishro, Pranaba K., Sanjay Agrawal, Rutuparna Panda, and Ajith Abraham. "A novel brightness preserving joint histogram equalization technique for contrast enhancement of brain MR images." Biocybernetics and Biomedical Engineering 41, no. 2 (2021): 540–53. http://dx.doi.org/10.1016/j.bbe.2021.04.003.

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47

Bakthula, Rajitha, and Suneeta Agarwal. "Radiographic X-ray Images Enhancement with Edge Preservation using Singular Value Decomposition." International Journal of Computational Physics Series 1, no. 1 (2018): 216–27. http://dx.doi.org/10.29167/a1i1p216-227.

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Contrast enhancement is one of the important issues in Medical X-ray imaging since these image, in general, are of low contrast and luminance. In medical X-ray imaging system viewing the bone structure and soft tissues are important for better medical diagnosis. The accuracy of Medical diagnosis of a patient purely depends on the clarity of the image. Hence an X-ray image must be well enhanced at the same time edges must be preserved and highlighted while applying image pre-processing technique. This is a challenging task in literature. In literature many techniques had been proposed for impro

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48

Hakem Alsaeedi, Ali, Yarub Alazzawi, and Suha Mohammed Hadi. "Fast Dust Sand Image Enhancement Based on Color Correction and New Fuzzy Intensification Operators." International Journal of Innovative Computing 13, no. 1-2 (2023): 31–35. http://dx.doi.org/10.11113/ijic.v13n1-2.416.

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Images captured in dusty environments suffering from poor visibility and quality. Enhancement of these images such as sand dust images plays a critical role in various atmospheric optics applications. In this work, proposed a new model based on Color Correction and New Fuzzy Intensification Operators to enhance san dust images. The proposed model consists of three phases: correction of color shift, removal of haze, and enhancement of contrast and brightness. The color shift is corrected using a fuzzy intensification operator to adjust the values of U and V in the YUV color space. The Adaptive

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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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Lee, Jae-Won, and Sung-Hoon Hong. "Bi-Histogram Equalization based on Differential Compression Method for Preserving the Trend of Natural Mean Brightness." Journal of Broadcast Engineering 19, no. 4 (2014): 453–67. http://dx.doi.org/10.5909/jbe.2014.19.4.453.

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