High noise remote sensing image denoising was a difficult problem in the field of remote sensing research. In this paper

to improve the reconstruction quality of remote sensing images

an improved iterative wavelet thresholding (IWT) algorithm is proposed on the foundation of the classical compressed sensing iterative wavelet thresholding (IWT-CS) algorithm. First

an adaptive wavelet filtering operator was proposed to remove parts of image noise

which selects wavelet coefficients of the remote sensing image in the process of image sparsity transform. Second

a descending BayesShrink threshold was put forward to select the wavelet coefficients obtained again in each iteration. Finally

an improved group sparse total variation (IGSTV) method was utilized to adjust the obtained reconstructed image to further improve the reconstruction quality of the remote sensing image. The experimental result demonstrates that the proposed algorithm was superior to the IWT-CS algorithm in terms of denoising reconstruction

which could recover a high quality remote sensing image from high noise image

and the effectiveness of the proposed algorithm was validated. In addition

the proposed algorithm could effectively protect the edges

textures

and other important feature information in remote sensing image. Under low compression sampling ratio

the proposed algorithm could still obtain relatively high peak signal to noise ratio (PSNR) and visual quality. In the satellite receive station

the proposed algorithm can be directly used to reconstruct a clear remote sensing image using a small amount of received-noise remote sensing image data.