A Novel Remote Sensing Image Enhancement Method Using Unsharp Masking in NSST Domain

In order to deal with the pseudo-Gibbs phenomenon and noise interference in the image enhancement, a novel remote sensing image enhancement technique based on unsharp masking and non-subsampled shearlet transform (NSST) is proposed in this paper. The steps of the proposed model are described as follows: Firstly, the input image is decomposed into one low-frequency component and several high-frequency components by the NSST transform; Secondly, the weighted guided image filter is performed on the low-frequency component to improve the contrast of the image, and the hard thresholding is used to suppress the noise of the high-frequency components; Thirdly, the inverse non-subsampled shearlet transform is utilized to reconstruct the image; Finally, the unsharp masking model is performed on the reconstructed image, and the final enhanced image is obtained. Experimental results and comparison analysis demonstrate that the proposed framework outperforms others in terms of remote sensing image enhancement.