Thresholding iterative methods were 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 the 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 a 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 show that our methods provide accurate and fast restorations when compared with the state of the art.

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

Bianchi D.;
2016

Abstract

Thresholding iterative methods were 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 the 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 a 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 show that our methods provide accurate and fast restorations when compared with the state of the art.
Boundary artifacts
Linearized Bregman splitting
Regulation preconditioning
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11770/338905
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