Enhancement of hyperspectral remote sensing images based on improved fuzzy contrast in nonsubsampled shearlet transform domain

In order to deal with the pseudo-Gibbs phenomenon in the process of hyperspectral remote sensing image enhancement, a novel image enhancement method based on nonsubsampled shearlet transform (NSST) is proposed in this paper. The main motivation of this study is to adjust the coefficient of remote sensing image enhancement as a pattern recognition task. Firstly, the input image is decomposed into a low-frequency component and some high-frequency components by NSST decomposition; Secondly, the guided filter is applied to process the low-frequency component to improve the contrast, and the improved fuzzy contrast is used to suppress the noise of the high-frequency components; Thirdly, the processed coefficients of low-frequency and high-frequency are reconstructed by inverse nonsubsampled shearlet transform (INSST), and the final enhanced image is obtained. The experimental results demonstrate that the proposed approach has obvious advantages in terms of objective data and subjective vision.