Swarm intelligence optimized piecewise gamma corrected histogram equalization for dark image enhancement

•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 ope...

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Veröffentlicht in:Computers & electrical engineering 2018-08, Vol.70, p.462-475
Hauptverfasser: Singh, Himanshu, Kumar, Anil, Balyan, L.K., Singh, G.K.
Format: Artikel
Sprache:eng
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Zusammenfassung:•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. 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. [Display omitted]
ISSN:0045-7906
1879-0755
DOI:10.1016/j.compeleceng.2017.06.029