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.