Local Adaptive Image Equalization
Dr .Gopisetty.GuruKesavaDasu1, Professor, Department of IT,
KKR & KSR Institute of Technology, Vinjanampadu ,Guntur Dt., Andhra Pradesh.
Bommi Archana2, Chirumamilla Pranitha3, V.P.S Lahari4, Karri Archana5,
Kolisetty Jayasri6
2,3,4,5,6 UG Students, Department of IT,
KKR & KSR Institute of Technology, Vinjanampadu, Guntur Dt., Andhra Pradesh.
1 gurukesavadasu.it@kitsguntur.ac.in 2 bommiarchana2002@gmail.com,
2 3 pranitha.ch1706@gmail.com,
4 vetchalahari@gmail.com, 5 archanaa0331@gmail.com,
6jayasri3999@gmail.com
Abstract
This paper presents a comprehensive approach to image enhancement, targeting the enhancement of contrast and reduction of noise in digital images. Leveraging state-of-the-art algorithms, the proposed methodology encompasses a strategic pipeline. Initially, the images undergo Histogram Equalization, a fundamental technique, to globally enhance contrast. Building upon this foundation, Contrast Limited Adaptive Histogram Equalization (CLAHE) is applied to achieve localized contrast enhancement, ensuring optimal balance and preservation of image details. Furthermore, the Adaptive Gamma Correction with Weighting Distribution (AGCWD) algorithm is integrated to fine-tune the enhanced images, dynamically adjusting gamma values to suppress noise and amplify visual features.
The implementation harnesses Python with OpenCV and Flask frameworks, facilitating seamless integration and accessibility. Through rigorous experimentation and comparative analysis, the efficacy of the proposed approach is demonstrated, showcasing substantial improvements in image quality and fidelity. The findings underscore the practical utility and efficacy of the proposed image enhancement system, positioning it as a valuable tool for various real-world applications in image processing and computer vision domains.
Keywords: Image Enhancement ,Noise Reduction ,Histogram Equalization, CLAHE, Adaptive Gamma Correction, OpenCV Library, Flask Web Framework , Image Fidelity.
