Remote Sensing Image Denoising Based on Gaussian Curvature and Shearlet Transform

Abstract

Model-based image denoising methods are well suited for use as image processors in remote sensing systems such as satellites due to their well-developed mathematical theory and low computational cost, but these methods can often only deal with a single type of random noise. In this paper, based on the model-based image denoising techniques, a remote sensing image mixed noise denoising algorithm using Gaussian curvature in the image surface and shearlet transform is proposed. The Gaussian curvature filtering (GCF) is used to suppress the salt & pepper noise (SPN) in the image to get the first denoised image. The second denoised image is obtained by processing the coefficient matrices of the shearlet transform decomposition using statistical method and adaptive median filtering (AMF). Finally, the reconstructed image is again optimized by AMF to eliminate the residual SPN. Specially, we propose the goodness-of-fit (GOF) test denoising algorithm in the shearlet domain based on empirical distribution function (EDF) statistics to solve the problem of insufficient denoising by the traditional threshold function. Our method can effectively remove the noise and improve the visibility and usability of remote sensing images. Experiment results show that our proposed method has PSNR and MSE performance improvement compared with related model-based and learning-based methods.