Relative performance of histogram equalization and adaptive histogram equalization in enhancing low-contrast image
Publication details: Ghaziabad MAT Journals 2024Edition: Vol.3(3), Sep-DecDescription: 32-41pSubject(s): Online resources: In: Research & review : Machine learning and cloud computingSummary: Low-contrast images present challenges across various domains, such as computer vision, medical imaging, and digital photography, where essential details can be lost in underexposed or poorly lit conditions. Traditional contrast enhancement methods such as Histogram Equalization (HE) and Adaptive Histogram Equalization (AHE) have long been employed to enhance the visual quality of such images. HE provides a global approach to contrast enhancement by redistributing pixel intensity values to make the image histogram more uniform. At the same time, AHE improves upon this by applying contrast enhancement locally to small regions of the image, allowing for better results in images with varying lighting conditions.| Item type | Current library | Status | Barcode | |
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School of Engineering & Technology Archieval Section | Not for loan | 2025-1329 |
Low-contrast images present challenges across various domains, such as computer vision, medical imaging, and digital photography, where essential details can be lost in underexposed or poorly lit conditions. Traditional contrast enhancement methods such as Histogram Equalization (HE) and Adaptive Histogram Equalization (AHE) have long been employed to enhance the visual quality of such images. HE provides a global approach to contrast enhancement by redistributing pixel intensity values to make the image histogram more uniform. At the same time, AHE improves upon this by applying contrast enhancement locally to small regions of the image, allowing for better results in images with varying lighting conditions.
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