Non-linear weight adjustment in adaptive gamma correction for image contrast enhancement

• Published in: Multimedia tools and applications Vol. 80; no. 3; pp. 3835 - 3862
• Main Authors: Sengupta, Debapriya, Biswas, Arindam, Gupta, Phalguni
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.01.2021; Springer Nature B.V
• Subjects: Adaptive algorithms; Algorithms; Computer Communication Networks; Computer Science; Data Structures and Information Theory; Image analysis; Image contrast; Image enhancement; Multimedia Information Systems; Parameters; Separation; Special Purpose and Application-Based Systems; Weight; Adaptive gamma correction; Non-linear weight adjustment; Steepness parameter; Brightness adjustment; Contrast enhancement; Image enhancement
• ISSN: 1380-7501; 1573-7721
• DOI: 10.1007/s11042-020-09583-1

Image enhancement remains an intricate problem, crucial for image analysis. Several algorithms exist for the same. A few among these algorithms categorize images into different classes based on their statistical parameters and apply separate enhancement functions for each class. One such algorithm is the well-known adaptive gamma correction (AGC) algorithm. It works well for each class of images, but fails when the statistical parameters lie on the boundary of separation of two classes. We have developed an enhancement algorithm which can enhance images which lie on the boundary of separation equally well, as images which lie deep inside the boundary. The basic idea behind the algorithm is to combine the different enhancement functions of AGC using non-linear weight adjustments. Both contrast and brightness have been modified using these weight adjustments. We have conducted experiments on a data-set consisting of 9979 images. Results show that by using the proposed algorithm, average entropy of the enhanced images increases by 3.97 % and average root mean square ( rms ) increases by 14.29 % over AGC. Visual improvement is also perceivable.