Adaptive iterative approach for image restoration

Abstract

Image restoration deals with images in which information has been obscured or partially lost. Practical problems are usually ill-conditioned. In this paper, we present an adaptive, iterative approach for these kinds of problems. The proposed approach is implemented in space domain, and it updates a restored image and an estimate of the regularization parameters simultaneously at each iteration. No prior knowledge about the noise variance is assumed. A space-variant operator, which works based on local information, determines the regularization parameters in order to ensure the regularization is `tight' in the smooth regions but `loose' in edge regions. This approach can be used for more general and spatially varying cases. Linear and non-linear constraints can be incorporated into the iterations. These constraints are motivated by the objective of accomplishing restorations with reduced artifacts such as rings and filtered noise artifacts, and preventing sharp edges while enhancing detailed structures in images simultaneously. The performance of the method is analyzed and both visual inspections and numerical results are presented. For example, SNR improvement of ΔSNR = 8.48 dB has been achieved for the defocused 256 by 256 Cameraman image (7 by 7 2-D uniform blurring, BSNR = 40 dB) and ΔSNR = 3.72 dB has been obtained for the BSNR = 20 dB image.