TY  - GEN
AU  - Mantri, Shamla 
AU  - Dahale, Aarth Anant 
TI  - Relative performance of histogram equalization and adaptive histogram equalization in enhancing low-contrast image 
PY  - 2024///
CY  - Ghaziabad
PB  - MAT Journals
KW  - Artificial Intelligence & Machine Learning
N2  - 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
UR  - https://matjournals.net/engineering/index.php/RRMLCC/article/view/1065
ER  -