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Journal articles on the topic 'Histogram equalization techniques'

Author: Grafiati

Published: 5 June 2025

Last updated: 1 August 2025

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1

Yousuf, M. A., and M. R. H. Rakib. "An Effective Image Contrast Enhancement Method Using Global Histogram Equalization." Journal of Scientific Research 3, no. 1 (2010): 43. http://dx.doi.org/10.3329/jsr.v3i1.5299.

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Image enhancement is one of the most important issues in low-level image processing. Histograms are the basis for numerous spatial domain processing techniques. In this paper, we present a simple and effective method for image contrast enhancement based on global histogram equalization. In this method, at first input image is normalized by making the minimum gray level value to 0. Then the probability of each grey level is calculated from the available ROI grey levels. Finally, histogram equalization is performed on the input image based on the calculated probability density (or distribution)

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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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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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C. R, Prasanth. "Study of Various Histogram Equalization Techniques." IOSR Journal of Electronics and Communication Engineering 8, no. 1 (2013): 12–18. http://dx.doi.org/10.9790/2834-0811218.

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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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Sabarish, R. T., and R. Ramadevi. "Analysis and Comparison of Image Enhancement Technique for Improving PSNR of Lung Images by Unsharp Mask Filtering Technique over Histogram Equalization Technique." CARDIOMETRY, no. 25 (February 14, 2023): 825–31. http://dx.doi.org/10.18137/cardiometry.2022.25.825831.

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Aim: The goal of study in this image enhancement technique is to enhance both contrast and sharpness of an image simultaneously to improve PSNR. Materials and Methods: Both unsharp mask filter and novel histogram equalization techniques were implemented on lung images which were collected from kaggle software. Samples were considered as (N=30) for unsharp mask filtering and (N=30) for novel histogram equalization technique with total sample size calculated using clinical. com. As a result the total number of samples was calculated as 60. Matlab coding was written for extracting PSNR values of

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Bhaskara Rao, Jana, K. V G Srinivas, A. Siva kumar, and J. Beatrice Seventline. "Bi Histogram Equalization Based Image Enhancement with Bicubic Interpolation." ECS Transactions 107, no. 1 (2022): 1441–57. http://dx.doi.org/10.1149/10701.1441ecst.

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In image processing, enhancement histogram equalization is the widely used technique for contrast enhancement. However, this technique tends to change the brightness of the image. Here, the contrast and resolution of image were enhanced using the proposed Bi Histogram Equalization Based Image Enhancement with Bicubic Interpolation (BHBI) technique. Bi Histogram for contrast enhancement and bicubic interpolation for resolution enhancement has taken. Bi Histogram Equalization separates the input image's histogram into two based on input mean before equalizing them independently. Bicubic Interpol

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Vinod Karar, Rasna,. "Study of Brightness Preservation Histogram Equalization Techniques." IOSR Journal of Electronics and Communication Engineering 01, no. 01 (2016): 66–70. http://dx.doi.org/10.9790/2834-150106670.

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Surmayanti, Surmayanti, and Sumijan Sumijan. "Improving Digital Image Clarity: A Study on the Application of Histogram Equalization for Noise Correction." sinkron 8, no. 2 (2024): 1073–79. http://dx.doi.org/10.33395/sinkron.v8i2.13564.

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This study aims to improve the clarity of digital images by examining the application of the histogram equalization method for noise correction. Noise in digital images is often a major challenge in maintaining the clarity and authenticity of visual information. Histogram equalization has been recognized as an effective method in improving image contrast and reducing the effects of noise. In this research, we conducted experiments by applying histogram equalization techniques to various types of digital images that are affected by noise. We analyzed the results by comparing the clarity and qua

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Abbood, Alaa Ahmed, Mohammed Sabbih Hamoud Al-Tamimi, Sabine U. Peters, and Ghazali Sulong. "New Combined Technique for Fingerprint Image Enhancement." Modern Applied Science 11, no. 1 (2016): 222. http://dx.doi.org/10.5539/mas.v11n1p222.

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This paper presents a combination of enhancement techniques for fingerprint images affected by different type of noise. These techniques were applied to improve image quality and come up with an acceptable image contrast. The proposed method included five different enhancement techniques: Normalization, Histogram Equalization, Binarization, Skeletonization and Fusion. The Normalization process standardized the pixel intensity which facilitated the processing of subsequent image enhancement stages. Subsequently, the Histogram Equalization technique increased the contrast of the images. Furtherm

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Sabarish, R. T., and R. Ramadevi. "Analysis and Comparison of Image Enhancement Technique for Improving PSNR of Lung Images by Median Filtering over Histogram Equalization Technique." CARDIOMETRY, no. 25 (February 14, 2023): 818–24. http://dx.doi.org/10.18137/cardiometry.2022.25.818824.

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Aim: The main goal of this project is image enhancement to improve interpretability or perception of information in images for human viewers and also to provide better input for other automated image processing techniques. Materials and Methods: In this research different sources of lung images collected from Kaggle website were used. Samples were considered as (N=30) for median filtering and (N=30) for novel histogram equalization technique with total sample size calculated using clinical.com. As a result the total number of sample was calculated to be 60.Using SPSS Software and a standard da

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Amil, Feroz Mahmud, Shanto Rahman, Md Mostafijur Rahman, and Emon Kumar Dey. "Bilateral Histogram Equalization for Contrast Enhancement." International Journal of Software Innovation 4, no. 4 (2016): 15–34. http://dx.doi.org/10.4018/ijsi.2016100102.

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As image enhancement is a well discussed issue, various methods have already been proposed till to date. Some of these methods perform well for specific applications but most of the techniques suffer from artifacts due to the over or under enhancement. To mitigate this problem a new technique namely Bilateral Histogram Equalization for contrast enhancement (BHE) which uses Harmonic mean of the image to divide the histogram is introduced. BHE is evaluated in both qualitative and quantitative manner and the results show that BHE creates less artifacts on several standard images than other existi

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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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Das, Sourav, Tarun Gulati, and Vikas Mittal. "Histogram Equalization Techniques for Contrast Enhancement: A Review." International Journal of Computer Applications 114, no. 10 (2015): 32–36. http://dx.doi.org/10.5120/20017-2027.

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Kumar, Deepak. "Enhancement of images using various histogram equalization techniques." IOSR Journal of Electronics and Communication Engineering 8, no. 1 (2013): 38–41. http://dx.doi.org/10.9790/2834-0813841.

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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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Harichandana, M., V. Sowmya, V. V. Sajithvariyar, and R. Sivanpillai. "COMPARISON OF IMAGE ENHANCEMENT TECHNIQUES FOR RAPID PROCESSING OF POST FLOOD IMAGES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIV-M-2-2020 (November 17, 2020): 45–50. http://dx.doi.org/10.5194/isprs-archives-xliv-m-2-2020-45-2020.

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Abstract. Satellite images are widely used for assessing the areal extent of flooded areas. However, presence of clouds and shadow limit the utility of these images. Numerous digital algorithms are available for enhancing such images and highlighting areas of interest. These algorithms range from simple to complex, and the time required to process these images also varies considerably. For disaster response, it is important to select an algorithm that can enhance the quality of the images in relatively short time. This study compared the relative performance of five traditional (Histogram Equa

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Goni, Ibrahim, Yusuf Musa Malgwi, and Asabe Sandra Ahmadu. "Satellite Image Enhancement Using Histogram Equalization." Electrical Science & Engineering 5, no. 1 (2023): 9–20. http://dx.doi.org/10.30564/ese.v5i1.5234.

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Image enhancement is an indispensable technique in improving the quality, brightness, contrast and clarity of satellite images. The object that appears in images and variation caused by shadow, occlusion, camouflage in satellite images are the fundamental challenges posed by image enhancement techniques. The aim of this research work was to enhance satellite images of Sambisa using histogram equalization technique. MATLAB 2021 was used to implement the experiment. The results show that histogram equalization method has an excellent processing effect and it improved the brightness, contrast and

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Gupta, Amit. "A Survey on Image Enhancement Based Histogram Equalization Techniques." International Journal for Research in Applied Science and Engineering Technology V, no. X (2017): 395–401. http://dx.doi.org/10.22214/ijraset.2017.10057.

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M, Naga Lakshmi. "Satellite Color Image Enhancement by using Histogram Equalization Techniques." International Journal for Research in Applied Science and Engineering Technology 7, no. 8 (2019): 692–97. http://dx.doi.org/10.22214/ijraset.2019.8101.

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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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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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K., Vijila Rani, and Nisha M. "Comparative Analysis of Image Enhancement Quality Based on Domains." Journal of VLSI Design and Signal Processing 5, no. 2 (2019): 9–16. https://doi.org/10.5281/zenodo.2702081.

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<em>First method is spatial domain and the effective of four diverse image spatial techniques (histogram equalization, adaptive histogram, histogram matching, and unsharp masking) produce sharpening and smoothening of image. Secondly, frequency domain technique and the effective of three diverse image spatial techniques (bilateral, homo-morphic and trilateral filter) were examined to achieve low noise image. Finally, SVD,QR,SLANT and HADAMARD was examined whichincreased human visual. For the above techniques, different quality parameters are evaluated. From the above evaluation, the proposed m

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R.Karthika, Mrs, Harini S, and Manikandaprabhu T. "Advanced Image Enhancement Techniques for Improved Visual Quality." International Scientific Journal of Engineering and Management 04, no. 03 (2025): 1–6. https://doi.org/10.55041/isjem02362.

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Image enhancement improves image quality for better visualization in various fields, including medical imaging, satellite imagery, and digital photography. This study explores various enhancement techniques, including spatial and frequency domain methods, histogram equalization, contrast stretching, noise removal, and deep learning approaches. The paper provides a detailed analysis of each technique, their advantages, limitations, and applications in different fields. The integration of artificial intelligence (AI) and deep learning is reshaping the future of image enhancement, making it more

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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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Trongtirakul, Thaweesak, and Nattapong Phanthuna. "Image Enhancement Using Weighted Bi-Histogram Equalization." International Journal of Applied Mathematics and Informatics 15 (November 20, 2021): 98–101. http://dx.doi.org/10.46300/91014.2021.15.16.

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Image enhancement is one of using in various digital signal processing areas. Advances in microcontrollers, microcomputers and computers have developed traditional algorithms in order to improve the quality of the resulting image and have implied many avenues to the design of new innovations using various techniques. This paper proposes contrast enhancement using weighted bi-histogram equalization based on distributed area ratio. Moreover, this technique must use a weighted factor which is calculated by the ratio of the histogram distribution. Likewise, an original image will be equalized by t

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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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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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Mustafa, Wan Azani, and Mohamed Mydin M. Abdul Kader. "A Review of Histogram Equalization Techniques in Image Enhancement Application." Journal of Physics: Conference Series 1019 (June 2018): 012026. http://dx.doi.org/10.1088/1742-6596/1019/1/012026.

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Azam, MD Afaque. "A Review of Various Histogram Equalization Techniques for Image Enhancement." IOSR Journal of Electrical and Electronics Engineering 11, no. 04 (2016): 48–51. http://dx.doi.org/10.9790/1676-1104044851.

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Shefali, Dhingra, and Bhardwaj Shikha. "IMAGE ENHANCEMENT COMPARISON USING HISTOGRAM EQUALIZATION AND FUZZY LOGIC TECHNIQUES." INTERNATIONAL EDUCATIONAL JOURNAL OF SCIENCE AND ENGINEERING - IEJSE 7, no. 1 (2024): 13–14. https://doi.org/10.5281/zenodo.15606838.

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Image enhancement is a method of improving the quality of an image and contrast is a major aspect. Traditional methods of contrast enhancement like histogram equalization results in over/under enhancement of the image especially a lower resolution one. We aim at developing a new Fuzzy Inference System to enhance the contrast of the low resolution images overcoming the shortcomings of the traditional methods. Results obtained using both the approaches are compared.

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Baqer, Ismail Sh. "Image Quality Enhancing by Efficient Histogram Equalization." Wasit Journal of Engineering Sciences 2, no. 2 (2014): 47–58. http://dx.doi.org/10.31185/ejuow.vol2.iss2.29.

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A two Level Image Quality enhancement is proposed in this paper. In the first level, Dualistic Sub-Image Histogram Equalization DSIHE method decomposes the original image into two sub-images based on median of original images. The second level deals with spikes shaped noise that may appear in the image after processing. We presents three methods of image enhancement GHE, LHE and proposed DSIHE that improve the visual quality of images. A comparative calculations is being carried out on above mentioned techniques to examine objective and subjective image quality parameters e.g. Peak Signal-to-N

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Prajakta, D. Charjan, and P. R. Deshmukh Dr. "Underwater Image Enhancement Using Hybrid Techniques." International Journal of Research in Computer & Information Technology (IJRCIT) 8, no. 3 (2023): 84–98. https://doi.org/10.5281/zenodo.8237831.

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Underwater image enhancement is a crucial task in improving the visibility, quality, and details of images captured in aquatic environments. This study presents an overview of various techniques and methodologies employed for underwater image enhancement. This study also discusses the challenges associated with underwater imaging, including color distortion, low contrast, and the effects of light scattering and absorption. It highlights the importance of preprocessing steps such as color correction and noise reduction. The study also emphasizes the effectiveness of adaptive histogram equalizat

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Muztaba, Robiatul, Hakim L. Malasan, and Mitra Djamal. "Development of an automated Moon observation system using the ALTS-07 Robotic Telescope: 2. Progress report on standard contrast enhancement of Moon crescent image with OpenCV." Journal of Physics: Conference Series 2214, no. 1 (2022): 012004. http://dx.doi.org/10.1088/1742-6596/2214/1/012004.

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Abstract Vision is a continuous two-dimensional (2-D) image that can be perceived by human visual system. Mathematically, image is a 2-D function that expresses the intensity of light. This research introduces the fundamental ideas of computer vision. We used OpenCV (Open-Source Computer Vision) to solve automatic image processing techniques, especially enhancement of contrast crescent visibility. We described an image using feature vectors to characterize and numerically to quantify the contents of an image. Then, we compared several techniques for increasing image quality, such as Basic cont

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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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Liantoni, Febri, and Agus Santoso. "PERBAIKAN KONTRAS CITRA MAMMOGRAM PADA KLASIFIKASI KANKER PAYUDARA BERDASARKAN FITUR GRAY-LEVEL CO-OCCURRENCE MATRIX." SINTECH (Science and Information Technology) Journal 3, no. 1 (2020): 46–51. http://dx.doi.org/10.31598/sintechjournal.v3i1.528.

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In this era to recognize breast tumors can be based on mammogram images. This method will expedite the process of recognition and classification of breast cancer. This research was conducted classification techniques of breast cancer using mammogram images. The proposed model targets classification studies for cases of malignant, and benign cancer. The research consisted of five main stages, preprocessing, histogram equalization, convolution, feature extraction, and classification. For preprocessing cropping the image using region of interest (ROI), for convolution, median filter and histogram

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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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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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Singh, Himanshu, and Vivek Singh. "A Comparative Analysis on Histogram Equalization Techniques for Medical Image Enhancement." International Journal of Advanced Research in Computer Science and Software Engineering 7, no. 6 (2017): 364–70. http://dx.doi.org/10.23956/ijarcsse/v7i6/0269.

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Pandey, Kanchan, and Sapna Singh. "A Comparative Study of Histogram Equalization Techniques for Image Contrast Enhancement." International Journal of Engineering Trends and Technology 8, no. 6 (2014): 305–8. http://dx.doi.org/10.14445/22315381/ijett-v8p256.

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Vidyasaraswathi, H. N., and M. C. Hanumantharaju. "Gradient, Texture Driven Based Dynamic-Histogram Equalization For Medical Image Enhancement." International Journal of Biology and Biomedical Engineering 15 (July 22, 2021): 303–10. http://dx.doi.org/10.46300/91011.2021.15.36.

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In many clinical diagnostic measurements, medical images play some significant role but often suffer from various types of noise and low-luminance, which causes some notable changes in overall system accuracy with misdiagnosis rate. To improve the visual appearance of object regions in medical images, image enhancement techniques are used as potential pre-processing techniques. Due to its simplicity and easiness of implementation, histogram equalization is widely preferred in many applications. But due to its mapping function based image transformation during enhancement process affect the bio

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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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Hang, Yuan. "Thyroid Nodule Classification in Ultrasound Images by Fusion of Conventional Features and Res-GAN Deep Features." Journal of Healthcare Engineering 2021 (July 22, 2021): 1–7. http://dx.doi.org/10.1155/2021/9917538.

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In spite of the gargantuan number of patients affected by the thyroid nodule, the detection at an early stage is still a challenging task. Thyroid ultrasonography (US) is a noninvasive, inexpensive procedure widely used to detect and evaluate the thyroid nodules. The ultrasonography method for image classification is a computer-aided diagnostic technology based on image features. In this paper, we illustrate a method which involves the combination of the deep features with the conventional features together to form a hybrid feature space. Several image enhancement techniques, such as histogram

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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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Abstract:

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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Liu, Hui, and Xue Bin Liu. "Research on Images Restoration Method under the Foggy Environment." Advanced Materials Research 1070-1072 (December 2014): 2037–40. http://dx.doi.org/10.4028/www.scientific.net/amr.1070-1072.2037.

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Because of the atmospheric scattering phenomenon, weather atmospheric degraded images captured in foggy environment have poor contrast and visibility, it has seriously affected the quality of the images. So this paper analysis and find something different between the dark channel prior and the interpolating self-adaptive histogram equalization method based on physical and non-physical model. And using the histogram similarity evaluation is evaluated on them. Finally, further discussion are indicated on techniques challenges and future development.

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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. 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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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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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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C., Parameswari, Rajalakshmi G., Rathnamala S., Sivakumar G., Hemajeyasri P., and Sivasabitha K. "A Comprehensive Analysis of Preprocessing Techniques for Thermal Breast Image Processing." Journal of Ubiquitous Computing and Communication Technologies 7, no. 2 (2025): 110–32. https://doi.org/10.36548/jucct.2025.2.002.

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Comprehensive and effective breast cancer screening programs are essential diagnostic instruments for early detection, which are then followed by rigorous intervention initiatives. A promising method for conducting non-invasive testing is the combination of remote sensing and thermal imaging technologies. Convolutional neural networks (CNNs) are capable of effectively identifying aberrant histological characteristics shared by most breast cancers; however, their application in breast cancer diagnosis is surprisingly limited. An overview of preprocessing techniques for thermal breast image proc

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