结合小波变换和Contourlet变换的多尺度、多分辨的共性及这两种变换分别适合处理点奇异和线奇异的特点，提出了一种新的联合使用小波正则项和Contourlet正则项的遥感图像恢复算法。算法中逆问题的求解等价于一个无约束凸规划的求解问题，目标函数由观测图像的拟合优度和正则项组成，传统的正则项是一个函数，本文使用两个正则项函数，能更好地利用图像的先验知识。然后根据Besov空间的半范数等价于小波系数的范数这一原理，提出了基于小波变换的联合使用小波正则项和Contourlet正则项的两步迭代阈值算法。对遥感图像的恢复结果表明，该算法在改善的信噪比（ISNR）和相关系数（CORR）等评价指标上都有显著的改善。

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）.