针对多种退化因素的遥感图像复原问题,提出一种基于Bregman迭代的遥感图像消除不规则采样、去模糊和去噪总变差复原方法。在此基础上,结合非局部正则化方法,提出一种自适应计算非局部均值滤波器参数的方法。求解时使用交替最小化方法将复杂的复原问题分割为两个容易求解的子问题。实验结果表明,本文方法比其他基于Bregman迭代的方法收敛速度快、复原效果好,且加入非局部正则化后具有更好的纹理细节信息保持能力。

For remote sensing image restoration with a variety of degradation factors,we propose a Bregman iteration based image restoration algorithm for remote sensing images to eliminate the irregular sampling effect,debluring and denoising. Moreover,based on this algorithm, combined with nonlocal regularization,we propose a method to determine the nonlocal filter parameter adaptively. Using alternating minimization, we split the complex original problem into two sub problems that are easier to solve. Our experimental results show that the proposed algorithm has a faster convergence speed and better restoration results compared to other total variation and Bregman iteration based algorithms, and By adding the nonlocal regularization, it can keep the detail information better.