Swarm intelligence optimized piecewise gamma corrected histogram equalization for dark image enhancement. (August 2018)

• Record Type: Journal Article
• Authors: Singh, Himanshu; Kumar, Anil; Balyan, L.K.; Singh, G.K.
• Is Part Of: Computers & electrical engineering. Volume 70(2018)
• Page Start: 462
• Page End: 475
• Publication Date: 2018-08
• Subjects: Image quality evaluation -- Remotely sensed images -- Image quality enhancement -- Dynamic histogram equalization -- Adaptive gamma correction -- Particle swarm optimization -- Contrast enhancement; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2017.06.029
• Languages: English
• ISSNs: 0045-7906

Abstract

Highlights: Highly robust weighted framework for overall enhancement of dark satellite images. A novel adaptive swarm intelligence based piecewise gamma correction is proposed. Intensity transformation using complementary-gamma values collectively. Optimal involvement of exponential, linear and statistical operations for mapping. 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. Graphical abstract: