Blind Restoration of Remote Sensing Images by a Combination of Automatic Knife-Edge Detection and Alternating Minimization

Abstract

In this paper, a blind restoration method is presented to remove the blur in remote sensing images. An alternating minimization (AM) framework is employed to simultaneously recover the image and the point spread function (PSF), and an adaptive-norm prior is used to apply different constraints to smooth regions and edges. Moreover, with the use of the knife-edge features in remote sensing images, an automatic knife-edge detection method is used to obtain a good initial PSF for the AM framework. In addition, a no-reference (NR) sharpness index is used to stop the iterations of the AM framework automatically at the best visual quality. Results in both simulated and real data experiments indicate that the proposed AM-KEdge method, which combines the automatic knife-edge detection and the AM framework, is robust, converges quickly, and can stop automatically to obtain satisfactory results.