Combined the common properties of wavelet and Contourlet transforms (multi-scale

multi-resolution and the different properties of their adaptivity to point-singularity and line-singularity respectively) a novel remote sensing image restoration algorithm based on two-step iterative thresholding was proposed. Inverse problem can be regarded as a class of the convex unconstrained optimization problem. The objective function is composed of the believe measure to original image and regularization item. The traditional algorithm has only one regularization item

but we proposed two regularization items so as to better use the prior knowledge of the image. The theorem is that the half norm in Besov space is equal to the norm in wavelet domain. Then the two-step iterative thresholding algorithm combined with wavelet regularization item and contourlet regularization item is proposed. The method is received obvious effection in remote sensing image restoration. The experiment results on remote sensing image restoration show that the proposed method achieves significant improvement in improved signal-noise ratio（ISNR） and correlation coefficient（CORR）.