期刊
COMPUTERS & ELECTRICAL ENGINEERING
卷 70, 期 -, 页码 462-475出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.compeleceng.2017.06.029
关键词
Image quality evaluation; Remotely sensed images; Image quality enhancement; Dynamic histogram equalization; Adaptive gamma correction; Particle swarm optimization; Contrast enhancement
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. (C) 2017 Elsevier Ltd. All rights reserved.
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