Method of Remote Sensing Image Enhancement in NSST Domain Based on Multi-stages Particle Swarm Optimization

Abstract

To further improve the definition and contrast of remote sensing images, a method of remote sensing image enhancement in non-subsampled shearlet transform (NSST) domain is proposed based on multi-stages particle swarm optimization (MSPSO) algorithm and fuzzy sets. Firstly, the image to be enhanced is decomposed into a low-frequency sub-band and several high-frequency sub-bands through NSST. Secondly, the coefficients of high-frequency sub-bands are enhanced according to adaptive Bayesian threshold method and nonlinear gain function, while that of the low-frequency sub-band is processed by using the fuzzy enhancement method with its fuzzy parameters optimized by MSPSO algorithm. A comparison is made among the proposed method, bidirectional histogram equalization method, stationary wavelet transform method, non-subsampled contourlet transform (NSCT) adaptive threshold method and artificial bee colony (ABC) optimization method in NSCT domain in terms of the subjective visual effect and objective quantitative evaluation indices such as contrast gain, definition gain and information entropy. Experimental results show that the method proposed in this paper can effectively improve the contrast and definition of remote sensing images and enhance edges details with better visual effect.