Publication Type : Journal Article
Publisher : Elsevier
Source : Computers and Electrical Engineering (Elsevier),vol. 70,pp. 462-475, 2018. [Impact Factor: 4.3]
Url : https://www.sciencedirect.com/science/article/abs/pii/S0045790617318876
Campus : Coimbatore
School : School of Engineering
Department : Electronics and Communication
Year : 2018
Abstract : In this paper, a highly adaptive swarm intelligence optimized dark image enhancement approach is proposed for remotely sensed satellite images. Here, a weighted summation framework is suggested for imparting “on-demand entropy restoration and contrast enhancement”. This approach utilizes the benefits of both gamma correction and histogram equalization; and hence, overall image enhancement can be appropriately imposed without losing original image features, especially for dark satellite images. For further improvement, gamma correction is also employed in a piecewise manner, separately for dark as well as light pixel values, so that over-saturation and other related unnatural artifacts can be avoided. A suitable entropy and contrast based cost function is utilized, and its maximization is done by employing particle swarm optimization over a three-dimensional search space. The proposed approach is found to be highly appreciable for overall enhancement, preserving all the intrinsic visual details for a wide range of dark image database covering satellite as well as general images.
Cite this Research Publication : H. Singh, A. Kumar, L.K. Balyan and G.K. Singh, “Swarm intelligence optimized piecewise gamma corrected histogram equalization for dark image enhancement,” Computers and Electrical Engineering (Elsevier),vol. 70,pp. 462-475, 2018. [Impact Factor: 4.3]