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Journal articles on the topic 'CLAHE technique'

Author: Grafiati

Published: 5 June 2025

Last updated: 15 July 2025

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1

B. N, Madhukar. "Image Enhancement using CLAHE-DWT Technique." International Journal for Research in Applied Science and Engineering Technology 6, no. 5 (2018): 2076–81. http://dx.doi.org/10.22214/ijraset.2018.5340.

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DZULKIFLI, FAHMI AKMAL. "Identification of Suitable Contrast Enhancement Technique for Improving the Quality of Astrocytoma Histopathological Images." ELCVIA Electronic Letters on Computer Vision and Image Analysis 20, no. 1 (2021): 84–98. http://dx.doi.org/10.5565/rev/elcvia.1256.

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Contrast enhancement plays an important part in image processing. In histology, the application of a contrast enhancement technique is necessary since it can help pathologists in diagnosing the sample slides by increasing the visibility of the morphological and features of cells in an image. Various techniques have been proposed to enhance the contrast of microscopic images. Thus, this paper aimed to study the effectiveness of contrast enhancement techniques in enhancing the Ki67 images of astrocytoma. Three contrast enhancement techniques consist of contrast stretching, histogram equalization

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Naser, Ahmed. "A proposed CLCOA Technique Based on CLAHE using Cat Optimized Algorithm for Plants Images Enhancement." Wasit Journal of Computer and Mathematics Science 3, no. 1 (2024): 18–27. http://dx.doi.org/10.31185/wjcms.202.

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Image Enhancement is one of the mainly significant with complex techniques in image study. The purpose of image enhancement is to advance the optical presence of an image, or to support a “improved convert representation for future mechanized image processing. Various images similar medical images, satellite images, natural with even real life photographs which have a lowly contrast and noise. This study presents a new enhancement technique based on standard contrast limited adaptive histogram equalization (CLAHE) technique for image enhancement which its name CLCOA. The suggested technique de

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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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Yakno, Marlina, Junita Mohamad-Saleh, and Mohd Zamri Ibrahim. "Dorsal Hand Vein Image Enhancement Using Fusion of CLAHE and Fuzzy Adaptive Gamma." Sensors 21, no. 19 (2021): 6445. http://dx.doi.org/10.3390/s21196445.

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Enhancement of captured hand vein images is essential for a number of purposes, such as accurate biometric identification and ease of medical intravenous access. This paper presents an improved hand vein image enhancement technique based on weighted average fusion of contrast limited adaptive histogram equalization (CLAHE) and fuzzy adaptive gamma (FAG). The proposed technique is applied using three stages. Firstly, grey level intensities with CLAHE are locally applied to image pixels for contrast enhancement. Secondly, the grey level intensities are then globally transformed into membership p

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Momoh, Muyideen Omuya. "LWT-CLAHE Based Color Image Enhancement Technique: An Improved Design." Computer Engineering and Applications Journal 9, no. 2 (2020): 117–26. http://dx.doi.org/10.18495/comengapp.v9i2.329.

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Color image enhancement is one of important process and actually a vital precursory stage to other stages in the field of digital image processing. This is due to the fact that the effectiveness of processes in this stage on the output determines the success of other stages for a quality overall performance. This paper presents a color image enhancement technique using lifting wavelet transform (LWT) and contrast limited adaptive histogram equalization (CLAHE) to overcome the issue of noise amplification, over and under-enhancement in exiting enhancement techniques. Test images from Computer V

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Bhargavi, S., B. Sadhvik Reddy, T. Sumanth Reddy, T. Sushma, S. Narendra Reddy, and P. Sai Kusuma. "Detection of Illegal Goods using X-ray Image Enhancement Algorithm." International Journal for Research in Applied Science and Engineering Technology 12, no. 4 (2024): 1522–32. http://dx.doi.org/10.22214/ijraset.2024.60081.

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bstract: An X-ray image enhancement technique integrating USM+CLAHE+HAZEREMOVAL and YOLOV2 for object detection is presented to address the problem of colour distortion in CLAHE enhanced airport security X-ray images. Calculating the grayscale images on the R, G, and B channels of the X-ray image and applying CLAHE enhancement to each, then merging the enhanced R, G, and B grayscale images will take place. After that, USM sharpening operation is applied to the CLAHE-enhanced X-ray image, and then it is merged with the original and USM-sharpened images according to the weight. Later haze remova

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Madhavi V., Vijaya, and P. Lalitha Surya Kumari. "A Qualitative Approach for Enhancing Fundus Images with Novel CLAHE Methods." Engineering, Technology & Applied Science Research 15, no. 1 (2025): 20102–7. https://doi.org/10.48084/etasr.9525.

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Glaucoma is a progressive eye disease. This study presents a custom technique to enhance retinal fundus images to detect glaucoma. Contrast enhancement is a crucial stage in medical image analysis to improve the visual impression of diseases. CLAHE is a common technique to improve images. Clip Limit (CL) and subimages may restrict the potential benefits of the typical approach and pose difficulties. This study introduces Enhanced CLAHE and Automated CLAHE to address the shortcomings of the base method. These methods demonstrate progress in improving retinal landmarks in various ways by looking

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Khozaimi, Ach, Isnani Darti, Syaiful Anam, and Wuryansari Muharini Kusumawinahyu. "Advanced cervical cancer classification: enhancing pap smear images with hybrid PMD filter-CLAHE." Indonesian Journal of Electrical Engineering and Computer Science 39, no. 1 (2025): 644. https://doi.org/10.11591/ijeecs.v39.i1.pp644-655.

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Cervical cancer remains a significant health problem, especially in developing countries. Early detection is critical for effective treatment. Convolutional neural networks (CNN) have shown promise in automated cervical cancer screening, but their performance depends on pap smear image quality. This study investigates the impact of various image preprocessing techniques on CNN performance for cervical cancer classification using the SIPaKMeD dataset. Three preprocessing techniques were evaluated: PeronaMalik diffusion (PMD) filter for noise reduction, contrast-limited adaptive histogram equali

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Hashmi, Adeel, Abhinav Juneja, Naresh Kumar, et al. "Contrast Enhancement in Mammograms Using Convolution Neural Networks for Edge Computing Systems." Scientific Programming 2022 (April 11, 2022): 1–9. http://dx.doi.org/10.1155/2022/1882464.

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A good contrast is significant for analysis of medical images, and if the images have poor contrast, then some methods of contrast enhancement can be of much benefit. In this paper, a convolution neural network-based transfer learning approach is utilized for contrast enhancement of mammographic images. The experiments are conducted on ISP and MIAS datasets, where ISP dataset is used for training and MIAS dataset is used for testing (contrast enhancement). Experimental comparison of the proposed technique is done with the most popular direct and indirect contrast enhancement techniques such as

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Archana, N., S. Mahalakshmi, R. Dhanagopal, and R. Menaka. "Absolute Transformation and Clahe Based High Performance Lucid Proposal for Image Processing." Journal of Computational and Theoretical Nanoscience 17, no. 8 (2020): 3660–70. http://dx.doi.org/10.1166/jctn.2020.9251.

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Image fusion is a one of the enhancement technique which is used to take the decision the images by the various types of sensors. Image fusion is nothing but the combination of two images which is helps to improve the quality of the image. In this paper, visible image and Infrared image are combined to acquire the informative image. Before and after image fusion, a new transformation technique is introduced to improve the quality of the image. To prove the quality of the image after applying new transformation technique, the fusion is done by four different techniques is used like Discrete Cos

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Mohd-Isa, Wan-Noorshahida, Joel Joseph, Noramiza Hashim, and Nbhan Salih. "Enhancement of digitized X-ray films using Contrast-Limited Adaptive Histogram Equalization (CLAHE)." F1000Research 10 (October 15, 2021): 1051. http://dx.doi.org/10.12688/f1000research.73236.1.

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Background: Rural clinics still have X-ray facilities that produce physical films, which are sent to the nearest hospital for evaluation. Purchasing digitalization facilities is costly, thus, sending digitized films to the radiologist may be a solution. This can be achieved via digital photo capture. However, there can be different output resolutions that may not be optimized for online diagnosis. This paper investigates if digitized X-ray films can be enhanced using image processing techniques of Contrast-Limited Adaptive Histogram Equalization (CLAHE), Normalized-CLAHE (N-CLAHE) and Min-Max

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R, Priyah R., and S. Kamalakkannan. "Hybrid contrast-limited adaptive histogram equalization and Deep Learning techniques for improving liver tumor detection." Future Technology 4, no. 3 (2025): 67–75. https://doi.org/10.55670/fpll.futech.4.3.7.

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Deep Learning and advanced image processing can enhance the detection and prognosis of liver cancer using medical imaging, such as Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) scans. Liver cancer detection is a challenging task due to factors such as poor contrast, noise in imaging techniques, limited annotated datasets, and the complex characteristics of tumors. This study proposes a hybrid technique that combines Contrast-Limited Adaptive Histogram Equalization (CLAHE), Convolutional Neural Networks (CNNs), Generative Adversarial Networks (GANs), and Transfer Learning (TL) t

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Khanbari, Shada Omer, and Adel Sallam M. Haider. "Enhanced Mammography image for Breast cancer detection using LC-CLAHE technique." University of Aden Journal of Natural and Applied Sciences 24, no. 1 (2022): 143–54. http://dx.doi.org/10.47372/uajnas.2020.n1.a12.

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Breast cancer is the greatest challenging health complexities that medical science is facing. Most cases can be prevented by early detection and diagnosis which are the best way to cure breast cancer to decrease the mortality rate. The aim of this research is to obtain a method for enhancing the mammography images by using the proposed method which is incorporating the Local Contrast with Contrast Limited Adaptive Histogram Equalization (LC-CLAHE) to improve the appearance and to increase the contrast of the image and then de-noised by 2D wiener filter techniques. To extract the region of inte

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Vinoothna, Boppudi. "Design and Development of Contrast-Limited Adaptive Histogram Equalization Technique for Enhancing MRI Images by Improving PSNR, UIQI Parameters in Comparison with Median Filtering." ECS Transactions 107, no. 1 (2022): 14819–27. http://dx.doi.org/10.1149/10701.14819ecst.

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Image enhancement is used to improve the quality of images and it enhances, sharpens image features, such as edges, boundaries, and contrast, to make a graphic display useful for display and analysis. In order to enhance the quality of MRI images, histogram-based image enhancement technique is developed in this work. Materials and Methods: In this research, a Contrast Limited Adaptive Histogram Equalization (CLAHE) based image enhancement technique is proposed and developed for MRI images and the proposed work is compared with another image enhancement technique called Median Filtering (MF) me

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Lahcene, Mohamed Rida, Mohammed Sofiane Bendelhoum, Bendjillali Ridha Ilyas, Bahidja Boukenadil, and Kamline Miloud. "Enhanced facial expression recognition using transfer learning and M-CLAHE." STUDIES IN ENGINEERING AND EXACT SCIENCES 5, no. 2 (2024): e9789. http://dx.doi.org/10.54021/seesv5n2-337.

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In recent years, Facial Expression Recognition (FER) techniques have gained substantial attention within the realm of biometric technology due to their wide range of applications, including emotion analysis, human-computer interaction, and surveillance systems. This paper presents a robust and efficient FER system composed of three key steps. First, precise face detection is performed using the Viola-Jones algorithm, a well-established method for detecting facial features in real-time. Second, the detected images are enhanced using a Modified Contrast Limited Adaptive Histogram Equalization (M

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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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Albahari, Elmaliana, Hizmawati Madzin, and Mohamad Roff Mohd Noor. "Fusion CLAHE-Based Image Enhancement with fuzzy Set Theory on Field Images." International Journal of Engineering & Technology 7, no. 4.31 (2018): 465–68. http://dx.doi.org/10.14419/ijet.v7i4.31.23730.

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In this paper, a new fusion of Contrast-Limited Adaptive Histogram Equalisation or CLAHE-based method is proposed to enhance field images. The field images, which are low resolution images, were taken using a camera or other devices such as smartphones with lower quality as compared to the lab images with proper setup. The field images had low contrast and were blurred and unsharp due to inconsistent setting or environment exposures. Image enhancement helps to enrich the perception of images for better quality, reduce impulsive noise, and sharpen the edges with the help of different image enha

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Vrochidis, Alexandros, Dimitrios Tzovaras, and Stelios Krinidis. "Enhancing 3D Reconstructions in Underwater Environments: The Impact of Image Enhancement on Model Quality." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-2/W10-2025 (July 7, 2025): 317–24. https://doi.org/10.5194/isprs-archives-xlviii-2-w10-2025-317-2025.

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Abstract. 3D reconstructions in underwater environments face significant challenges due to poor image quality, caused by blurring, reduced contrast, color distortion, and inadequate lighting. This study investigates the impact of various image enhancement techniques on underwater 3D reconstruction, focusing on Contrast Limited Adaptive Histogram Equalization (CLAHE), RGB Histogram Stretching (RGHS), and a combined approach integrating RGB stretching with CLAHE. Three real-world underwater datasets were analyzed to assess the effectiveness of these methods in improving the accuracy and complete

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Naknaem, Kasatapad, and Titipong Kaewlek. "A comparative study of pre-processing methods to improve glioma segmentation performance in brain MRI using deep learning." Journal of Associated Medical Sciences 57, no. 2 (2024): 132–40. http://dx.doi.org/10.12982/jams.2024.035.

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Background: Glioma is the most common brain tumor in adult patients and requires accurate treatment. The delineation of tumor boundaries must be accurate and precise, which is crucial for treatment planning. Currently, delineating boundaries for tumors is a tedious, time-consuming task and may be prone to human error among oncologists. Therefore, artificial intelligence plays a vital role in reducing these problems. Objective: This study aims to find a relationship between improving image enhancement and evaluating the performance of deep learning models for segmenting glioma image data on bra

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Mapayi, Temitope, Serestina Viriri, and Jules-Raymond Tapamo. "Comparative Study of Retinal Vessel Segmentation Based on Global Thresholding Techniques." Computational and Mathematical Methods in Medicine 2015 (2015): 1–15. http://dx.doi.org/10.1155/2015/895267.

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Due to noise from uneven contrast and illumination during acquisition process of retinal fundus images, the use of efficient preprocessing techniques is highly desirable to produce good retinal vessel segmentation results. This paper develops and compares the performance of different vessel segmentation techniques based on global thresholding using phase congruency and contrast limited adaptive histogram equalization (CLAHE) for the preprocessing of the retinal images. The results obtained show that the combination of preprocessing technique, global thresholding, and postprocessing techniques

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

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

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Mahesh, Manik Kumbhar, and B. Godbole Bhalchandra. "Dehazing Effects on Image and Videousing AHE, CLAHE and Dark Channel Prior." International Journal of Engineering and Advanced Technology (IJEAT) 9, no. 3 (2020): 119–25. https://doi.org/10.35940/ijeat.C4833.029320.

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The image captured by camera is degraded by various atmospheric parameters for example rain, storm, wind, haze, snow. The removing haze is called dehazing, is naturally done in the physical degradation model that requires a resolution of an ill-posed inverse problem. In this paper discussion and e relative study of Adaptive Histogram Equalization (AHE) as well as Contrast limited adaptive histogram equalization (CLAHE) and dark channel prior (DCP). This article suggest image and video dehazing technique working on DCP method. The DCP is resulted from the characteristics of images taken in outd

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Pranika and Amandeep. "Image Enhancement using Hybrid Convolutional Autoencoder with Clahe Post Processing." International Journal of Enhanced Research in Management & Computer Applications 14, no. 06 (2025): 59–68. https://doi.org/10.55948/ijermca.2025.0611.

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In computer vision, improving low-resolution images is necessary when compression, sensor fails or outside conditions result in image degradation. The author introduces a hybrid technique that uses a Convolutional Autoencoder (CAE) and Contrast Limited Adaptive Histogram Equalization (CLAHE) to enhance both image sharpness and local contrast. The architecture of the CAE is set up to map small, pixelated images into good reconstructions using convolutional layers with sigmoid code, max pooling, upsampling and adding shortcuts that help keep important low-level features. For this task, input pho

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Hamdani, Ibnu Mansyur, Ismi Rizqa Lina, and Muhammad Takdir Muslihi. "Deteksi Tepi Optimal dengan Integrasi Canny, CLAHE, dan Perona-Malik Diffusion Filter." Jurnal Mosfet 5, no. 1 (2025): 127–36. https://doi.org/10.31850/jmosfet.v5i1.3638.

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Edge detection is a fundamental technique in digital image processing, crucial for identifying object boundaries. However, detecting edges in low-intensity and noisy images remains a significant challenge. This study proposes an optimal edge detection method by integrating the Canny algorithm, Contrast Limited Adaptive Histogram Equalization (CLAHE), and Perona-Malik Diffusion Filter, with automatic kappa (k) value determination using the Fractional Order Sobel Mask. The process begins with noise reduction through the Perona-Malik Diffusion Filter, followed by local contrast enhancement using

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Khan, Khan Bahadar, Amir A. Khaliq, Muhammad Shahid, and Sheroz Khan. "AN EFFICIENT TECHNIQUE FOR RETINAL VESSEL SEGMENTATION AND DENOISING USING MODIFIED ISODATA AND CLAHE." IIUM Engineering Journal 17, no. 2 (2016): 31–46. http://dx.doi.org/10.31436/iiumej.v17i2.611.

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Retinal damage caused due to complications of diabetes is known as Diabetic Retinopathy (DR). In this case, the vision is obscured due to the damage of retinal tinny blood vessels of the retina. These tinny blood vessels may cause leakage which affect the vision and can lead to complete blindness. Identification of these new retinal vessels and their structure is essential for analysis of DR. Automatic blood vessels segmentation plays a significant role to assist subsequent automatic methodologies that aid to such analysis. In literature most of the people have used computationally hungry a st

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Mehrabi, Mohsen, and Nafise Salek. "Enhancing diagnostic accuracy in breast cancer: integrating novel machine learning approaches with enhanced image preprocessing for improved mammography analysis." Polish Journal of Radiology 89 (January 8, 2025): 573–83. https://doi.org/10.5114/pjr/195523.

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PurposeThis study explored the use of computer-aided diagnosis (CAD) systems to enhance mammography image quality and identify potentially suspicious areas, because mammography is the primary method for breast cancer screening. The primary aim was to find the best combination of preprocessing algorithms to enable more precise classification and interpretation of mammography images because the selected preprocessing algorithms significantly impact the effectiveness of later classification and segmentation processes.Material and methodsThe study utilised the mini-MIAS database of mammography ima

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Pramunendar, Ricardus, Dwi Prabowo, Dewi Pergiwati, Yuslena Sari, Pulung Andono, and Moch Soeleman. "New Workflow for Marine Fish Classification Based on Combination Features and CLAHE Enhancement Technique." International Journal of Intelligent Engineering and Systems 13, no. 4 (2020): 293–304. http://dx.doi.org/10.22266/ijies2020.0831.26.

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Kuna, Sril Lxmi, and A. V. Krishna Prasad. "Deep Learning Empowered Diabetic Retinopathy Detection and Classification using Retinal Fundus Images." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 1 (2023): 117–27. http://dx.doi.org/10.17762/ijritcc.v11i1.6058.

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Diabetic Retinopathy (DR) is a commonly occurring disease among diabetic patients that affects retina lesions and vision. Since DR is irreversible, an earlier diagnosis of DR can considerably decrease the risk of vision loss. Manual detection and classification of DR from retinal fundus images is time-consuming, expensive, and prone to errors, contrasting to CAD models. In recent times, DL models have become a familiar topic in several applications, particularly medical image classification. With this motivation, this paper presents new deep learning-empowered diabetic retinopathy detection an

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Şengöz, Nilgün, Gül Karaman, and Mert Samet Çeliker. "OPTIMIZING ORAL CANCER DETECTION: ENHANCING RESNET50 WITH CLAHE FOR IMPROVED CLASSIFICATION ACCURACY." Mugla Journal of Science and Technology 11, no. 1 (2025): 1–10. https://doi.org/10.22531/muglajsci.1565902.

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The Histopathologic Oral Cancer Detection Dataset, which consists of meticulously annotated high-resolution histopathological images, is an essential resource for advancing the early diagnosis and classification of oral cancer. The dataset, categorized into "Normal" and "Oral Squamous Cell Carcinoma (OSCC)" classes, underpins the development and evaluation of sophisticated deep learning models, particularly Convolutional Neural Networks (CNNs), designed to distinguish between malignant and non-malignant tissue samples. In this study, the efficacy of the ResNet50 deep learning architecture was

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Hassan, Buraq Abed Ruda, and Faten Abed Ali Dawood. "Face-based Gender Classification Using Deep Learning Model." Journal of Engineering 30, no. 01 (2024): 106–23. http://dx.doi.org/10.31026/j.eng.2024.01.07.

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Gender classification is a critical task in computer vision. This task holds substantial importance in various domains, including surveillance, marketing, and human-computer interaction. In this work, the face gender classification model proposed consists of three main phases: the first phase involves applying the Viola-Jones algorithm to detect facial images, which includes four steps: 1) Haar-like features, 2) Integral Image, 3) Adaboost Learning, and 4) Cascade Classifier. In the second phase, four pre-processing operations are employed, namely cropping, resizing, converting the image from(

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Kurniawan, Rudi, and Lukman Sunardi. "Integration of Image Enhancement Technique with DenseNet201 Architecture for Identifying Grapevine Leaf Disease." MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer 24, no. 2 (2025): 333–46. https://doi.org/10.30812/matrik.v24i2.4137.

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Early detection of grapevine leaf diseases is crucial for maintaining both the quality and quantity of grape production. Manual identification methods are often ineffective and prone to errors. This research aims to develop a precise and efficient method for classifying grapevine leaf diseases using Contrast Limited Adaptive Histogram Equalization (CLAHE) and the DenseNet201 Deep Convolutional Neural Network (DCNN) architecture. The research methodology involves collecting a dataset of grapevine leaf images affected by black measles, black rot, and leaf blight alongside healthy leaves. Followi

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Alagulskhmi, A. "IMAGE RECOGNITION AND IDENTIFICATION USING MACHINE LEARNING." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, no. 12 (2023): 1–9. http://dx.doi.org/10.55041/ijsrem27729.

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Face recognition has been a rapidly growing and intriguing region progressively applications. A huge number of face recognition calculation have been produced in a long time ago. In this paper, for face detection we are using HOG (Histogram of oriented Gradient) based face detector which gives more accurate results rather than other machine learning algorithms like Haar Cascade. In recognition process we are using CLAHE (Contrast Limited Adaptive Histogram equalization) for pre-processing than we are using HOG which is a standard technique for features extraction. HOG features are extracted fo

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Agarwal, Dimple, and Sharmishta Desai. "Literature Review on X-Ray Image Enhancement." Journal of Innovations in Data Science and Big Data Management 2, no. 1 (2023): 1–8. http://dx.doi.org/10.46610/jidsbdm.2023.v02i01.001.

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In common, a raw X-ray image holds bad quality of the image if obtained directly coming out of a digital flat detector. It isn't satisfactory for treatment planning as well as diagnosis. For this image, enhancement is required. There are distinct ways through which image enhancement takes place. It is one of the preprocessing techniques helpful for moving further into treatment planning. Common methodologies like N-CLAHE which works with local enhancement and global enhancement can make an image look more extensive and more realistic. This technique consists of two main steps. Firstly, intensi

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Sai, Varkala Tarun, Nallam Eswara Sai Akhil, Tandra Jaya Mallika Jashnavi, and Naga Venkata Kashim Kanakala. "Image Quality Enhancement for Wheat rust Diseased Leaf Image using Histogram Equalization & CLAHE." E3S Web of Conferences 391 (2023): 01029. http://dx.doi.org/10.1051/e3sconf/202339101029.

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In the domain of agriculture, few crops play an important role as wheat is one of them. It is one of the most important one’s across the globe. Nearly providing 15% food production across the world, it is also a winter cereal crop and a most essential food. The real challenge is to enhance the images of wheat crop in the agricultural area. because some of these are captured in real space environments may not be that clear to predict the type of disease of the crop that it is suffering from. So, we enhance the captured images using few existing techniques using the image histograms and the furt

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Pertiwi, Marisha, Fortia Magfira, Dwi Rahmaisyah, and M. Hasbi Sidqi Alajuri. "Improving the Quality of X-Ray Images of the Lungs of COVID-19 and Healthy Patients Using the Contrast Limited Adaptive Histogram Equalization (CLAHE) Method in Batam." JEECS (Journal of Electrical Engineering and Computer Sciences) 10, no. 1 (2025): 19–30. https://doi.org/10.54732/jeecs.v10i1.3.

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X-ray imaging is a widely used technique for observing lung patients conditions. Compared to other radiographic methods, X-ray is more accessible, cost-effective, and commonly available in healthcare facilities. However, digital X-ray images often suffer from low quality, particularly in terms of image contrast, which complicates the process of identifying lung abnormalities accurately. In Embung Fatimah Hospital in Batam, X-ray imaging is routinely used to screen COVID-19 and healthy patients. To address the issue of poor image contrast, this study applies the Contrast Limited Adaptive Histog

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Khan, Sajid Ali, Shariq Hussain, and Shunkun Yang. "Contrast Enhancement of Low-Contrast Medical Images Using Modified Contrast Limited Adaptive Histogram Equalization." Journal of Medical Imaging and Health Informatics 10, no. 8 (2020): 1795–803. http://dx.doi.org/10.1166/jmihi.2020.3196.

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The low contrast medical images seriously affect the clinical diagnosis process. To improve the image quality, we propose an effective medical images contrast enhancement technique in this paper. Shear wavelet transformation is used for decomposition of image components into low-frequency and high-frequency. The low-frequency part contrast is adjusted by applying modified contrast limited adaptive histogram equalization (CLAHE). The resultant image is further processed through technique of fuzzy contrast enhancement to maintain the spectral information of an image. Results of the experimentati

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Jung, Jin-Hyun. "FPGA implementation using a CLAHE contrast enhancement technique in the termal equipment for real time processing." Journal of the Korea Society of Computer and Information 21, no. 11 (2016): 39–47. http://dx.doi.org/10.9708/jksci.2016.21.11.039.

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Susilo, Devito, and Wahyono. "An Analysis of Image Enhancement Effects on Convolutional Neural Network-based Pulmonary Tuberculosis Detection." E3S Web of Conferences 465 (2023): 02054. http://dx.doi.org/10.1051/e3sconf/202346502054.

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Pulmonary Tuberculosis (TB) is a primary global infectious disease. Diagnosing TB patients involves medical examination and chest X-ray (CXR) imaging. This CXR image creates an opportunity to utilize machine learning to help physicians and radiologists diagnose TB suspects. Due to the inconsistency of image quality, image enhancement is one of the preprocessing steps to overcome the poor quality of the image. This study examines the effects of several image enhancement techniques, i.e., Histogram Equalization (HE), Contrast Limited Adaptive Histogram Equalization (CLAHE), and Fast Fourier Tran

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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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Zaheer, Sumbul. "A Triadic Approach for Enhancement of Underwater Images Using Adaptive Colour Correction with Unsharp Masking and CLAHE Implementation." International Journal for Research in Applied Science and Engineering Technology 12, no. 5 (2024): 5580–88. http://dx.doi.org/10.22214/ijraset.2024.62740.

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Abstract: The underwater domain has distinct challenges for capturing and examining images both. This is due to absorption and dispersion of light, which diminishes visual clarity and also distorts colour. In this context, we present an extensive method for enhancing underwater images with the objective of restoring true colours, uplifting contrast, and emphasizing minute details. Adaptive colour correction, detail sharpening, and contrast enhancement techniques drafted for underwater environments are all included in our project. Using objective picture quality standards includes the Underwate

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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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Akintola, Abimbola Ganiyat, Taye Oladele Aro, and Abdul-hafiz Taiwo Oniyangi. "Appearance-Based Feature Extraction Techniques for Facial Recognition: Comparative Study ." DIU Journal of Science & Technology 15, no. 1 (2024): 6–10. https://doi.org/10.5281/zenodo.13826889.

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One of the important steps that must be considered in developing a robust facial recognition is feature extraction. The rate of recognition in the facebased biometric system can be determined by the amount of measurable and relevant features extracted from the face image. Several feature extraction algorithms in appearance-based technique such as Linear Discriminant Analysis (LDA), Independent Analysis (LDA) and Principal Component Analysis (PCA) have been used in face recognition. This paper applied Contrast Limited Adaptive Histogram Equalization (CLAHE) before three appearancebased feature

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Safie, Sairul Izwan, and Puteri Zarina Megat Khalid. "Practical Consideration in using Pre-trained Convolutional Neural Network (CNN) for Finger Vein Biometric." International Journal of Online and Biomedical Engineering (iJOE) 19, no. 02 (2023): 163–75. http://dx.doi.org/10.3991/ijoe.v19i02.35273.

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Using a pre-trained Convolutional Neural Network (CNN) model for a practical biometric authentication system requires specific procedures for training and performance evaluation. There are two criteria for a practical biometric system studied in this paper. First, the system’s ability to handle identity theft or impersonation attacks. Second, the ability of the system to generate high authentication performance with minimal enrollment period. We propose the use of the Multiple Clip Contrast Limited Adaptive Histogram Equalization (MC-CLAHE) technique to process finger images before being train

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Sriraam, Natarajan, Leema Murali, Amoolya Girish, et al. "Classification of Breast Thermograms Using Statistical Moments and Entropy Features with Probabilistic Neural Networks." International Journal of Biomedical and Clinical Engineering 6, no. 2 (2017): 18–32. http://dx.doi.org/10.4018/ijbce.2017070102.

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Breast cancer is considered as one of the life-threatening disease among woman population in developing as well as developed countries. This specific study reports on classification of breast thermograms using probabilistic neural network (PNN) with four statistical moments features mean, standard deviation, skewness and kurtosis and two entropy features, Shannon entropy and Wavelet packet entropy. The CLAHE histogram equalization algorithm with uniform and Rayleigh distributions were considered for contrast enhancement of breast thermal images. The asymmetry detection was performed by applyin

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.., S., V. D. Ambeth .., R. Venkatesan, and S. Malathi. "Relevance Mapping based CNN model with OSR-FCA Technique for Multi-label DR Classification." Fusion: Practice and Applications 11, no. 2 (2023): 90–110. http://dx.doi.org/10.54216/fpa.110207.

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In computer vision, multi-label classification (MLC) is especially important for medical picture analysis. We use MLC to classify diverse stages of diabetic retinopathy (DR) using colour fundus pictures of varying brightness and contrast. As a result, ophthalmologists can now identify the early warning symptoms of DR and the varying stages of DR, allowing them to begin therapy sooner and prevent further difficulties. Using the outlier-based shallow regularization fuzzy clustering approach (OSR-FCA), for classification we present a deep learning method in this paper's picture segmentation task.

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Sathyan, Neethu M., and Sashi Rekha Karthikeyan. "Infrared Thermal Image Enhancement in Cold Spot Detection of Condenser Air Ingress." Traitement du Signal 39, no. 1 (2022): 323–29. http://dx.doi.org/10.18280/ts.390134.

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The cold spot identification approach is limited due to the lack of high-resolution infrared thermal images. To solve the problem, infrared thermal images are enhanced using several ways. To improve the thermal images for cold spot detection, researchers used CLAHE, the Canny edge detection method, and deep learning approaches based on denoising autoencoder. The comparison of several enhancement methods based on quality metric factors leads to the selection of the best method. The noise in the Infrared (IR) image is reduced by using a high-resolution autoencoder. The ability to convert a 32 ×

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Burhan, Iman Mohammed, Rahman Nahi Abid, Mustafa Abdalkhudhur Jasim, and Refed Adnan Jaleel. "Improved Methods for Mammogram Breast Cancer Using by Denoising Filtering." Webology 19, no. 1 (2022): 1481–92. http://dx.doi.org/10.14704/web/v19i1/web19099.

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In diagnosing breast cancer, digital mammograms have shown their effectiveness as an appropriate and simple instrument in the early detection of tumor. Mammograms offer helpful cancer symptoms information, including microcalcifications and masses, which are not easy to distinguish because there are some flaws with the mammography images, including low contrast, high noise, fuzzy and blur. Additionally, there is a major problem with mammography because of a high density of the breast which conceals As a result of the mammographic image, it is more difficult to distinguish between the tissues wi

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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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Toresa, Dafwen, Fana Wiza, Keumala Anggraini, Taslim Taslim, Edriyansyah, and Lisnawita Lisnawita. "Comparison of Image Enhancement Methods for Diabetic Retinopathy Screening." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 7, no. 5 (2023): 1111–17. http://dx.doi.org/10.29207/resti.v7i5.5193.

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The most common factor contributing to visual abnormalities that result in blindness is known as diabetic retinopathy (DR). Retinal fundus scanning, a non-invasive method that is integral to the picture pre-processing phase, can be used to identify and monitor DR. Low intensity, irregular lighting, and inhomogeneous color are some of the main issues with DR fundus photographs. Analysis of aberrant characteristics on retinal fundus pictures to identify diabetic retinopathy is one of the key responsibilities of image enhancement. However, a variety of approaches have been created, and it is unkn

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