Regularization preconditioners for frame-based image deblurring with reduced boundary artifacts

TL;DR

This paper introduces novel regularization preconditioners for frame-based image deblurring that effectively reduce boundary artifacts, improve restoration quality, and enhance computational efficiency compared to traditional methods.

Contribution

It proposes new preconditioning strategies for the modified linearized Bregman algorithm, combining nonstationary preconditioning with convergence guarantees, and demonstrates superior performance in image deblurring.

Findings

Improved image restoration quality with new preconditioners.

Reduced boundary artifacts in deblurred images.

Faster convergence and computational savings.

Abstract

Thresholding iterative methods are recently successfully applied to image deblurring problems. In this paper, we investigate the modified linearized Bregman algorithm (MLBA) used in image deblurring problems, with a proper treatment of the boundary artifacts. We consider two standard approaches: the imposition of boundary conditions and the use of the rectangular blurring matrix. The fast convergence of the MLBA depends on a regularizing preconditioner that could be computationally expensive and hence it is usually chosen as a block circulant circulant block (BCCB) matrix, diagonalized by discrete Fourier transform. We show that the standard approach based on the BCCB preconditioner may provide low quality restored images and we propose different preconditioning strategies, that improve the quality of the restoration and save some computational cost at the same time. Motivated by a recent nonstationary preconditioned iteration, we propose a new algorithm that combines such method with the MLBA.We prove that it is a regularizing and convergent method. A variant with a stationary preconditioner is also considered. Finally, a large number of numerical experiments shows that our methods provide accurate and fast restorations, when compared with the state of the art.