Remote sensing image denoising based on shearlet transform and goodness-of-fit test

Aiming at Gaussian white noise in remote sensing images, a remote sensing image denoising algorithm based on shearlet transform and goodness of fit test is proposed. Firstly, the noisy remote sensing image is decomposed into different sub-bands by shearlet transform, and the denoising threshold is estimated by using the statistical relationship of Gaussian white noise coefficients in shearlet domain; secondly, the goodness of fit test statistic of high-frequency sub-band is calculated, and the statistic is compared with the denoising threshold for denoising; finally, the coefficient matrix is inversely shearlet transformed to reconstruct the denoised image. Simulation experimental results show that the algorithm can effectively remove Gaussian noise in remote sensing images, maintain the edge texture information of the image, and obtain a higher peak signal-to-noise ratio under different noise levels, which is an average improvement of 0.33 dB compared with the shearlet threshold denoising algorithm.