โ Scribed by Aggelos K. Katsaggelos; Moon Gi Kang
No coin nor oath required. For personal study only.
This article develops an iterative spatially adaptive regularized image restoration algorithm. The proposed algorithm is based on the minimization of a weighted smoothing functional. The weighting matrices are defined as functions of the partially restored image at each iteration step. As a result, no prior knowledge about the image and the noise is required, but the weighting matrices as well as the regularization parameter are updated based on the restored image at every step. Conditions for the convexity of the weighted smoothing functional and for the convergence of the iterative algorithm are established for a unique global solution which does not depend on initial conditions. Experimental results are shown with astronomical images which demonstrate the effectiveness of the proposed algorithm.
๐ SIMILAR VOLUMES
Most operations in image processing involve application of a mask centered at each pixel in an image. Consequently, in order to preserve image size under various operations, it is necessary to extend an image outside its borders for the number of columns and rows equal to half the mask size. This pa