Remote sensing image contrast enhancement based on GA and curvelet transform

A remote sensing image contrast enhancement algorithm is proposed by combing genetic algorithm (GA) and discrete curvelet transform (DCT). A remote sensing image is decomposed by DCT In-complete Beta transform (IBT) is used to obtain non-linear gray transform curve so as to enhance the coefficients in the coarse scale in the DCT domain. GA determines optimal gray transform parameters. In order to avoid the expensive time for traditional contrast enhancement algorithms, which search optimal gray transform parameters in the whole parameters space, based on gray distribution of an image, a classification criterion is used to contrast type of input image. Parameters space is respectively determined according to different contrast types, which greatly shrinks parameters space. Thus searching direction of GA is guided by the new parameter space. Considering the drawback of traditional histogram equalization that it reduces the information and enlarges noise and background butter in the processed image, a synthetic objective function is used as fitness function of GA. combing peak signal-noise-ratio (PSNR) and information entropy. Inverse DCT is done to obtain final enhanced image. Experimental results show that the new algorithm is able to well enhance the contrast for the remote sensing image while keeping the noise and background butter from being greatly enlarged.