Abstract: In order to realize the fast deblurring for optical remote sensing images，the disadvantages of the original total variation regularization(TV) based algorithm are studied．A surrogate cost functional based on TV model is proposed. The proposed model was first transformed into three minimizing sub-problems with close form solution and then a variable splitting based fast algorithm is presented. Meanwhile, to overcome the “staircase effect”, the regularization parameters are estimated and adaptively adjusted by perceptual sensitivity to different areas such as flat and edge areas. Experiments show that the proposed algorithm is superior to state-of-the-art algorithms such as Wiener filter, restricted least-squares filter, TV based gradient decreasing algorithm and alternative subspace projection algorithm with respect to signal to noise ratio and the computing time. In addition, both the Gibbs effect and staircase effect are reduced by the proposed algorithm without the loss of image information．

Key words: information processing, remote image processing, recovery algorithm, total variation, surrogate cost function, fast algorithm