Remote Sensing Image Denoising Based on Gaussian Curvature and Shearlet Transform

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