Blind motion deblurring using multiple images
Journal of Computational Physics
A Unified Primal-Dual Algorithm Framework Based on Bregman Iteration
Journal of Scientific Computing
A Singular Value Thresholding Algorithm for Matrix Completion
SIAM Journal on Optimization
Bregmanized Nonlocal Regularization for Deconvolution and Sparse Reconstruction
SIAM Journal on Imaging Sciences
Deconvolving Poissonian images by a novel hybrid variational model
Journal of Visual Communication and Image Representation
Wavelet frame based surface reconstruction from unorganized points
Journal of Computational Physics
Analysis and Generalizations of the Linearized Bregman Method
SIAM Journal on Imaging Sciences
SIAM Journal on Imaging Sciences
Adaptive Multiresolution Analysis Structures and Shearlet Systems
SIAM Journal on Numerical Analysis
Multidimensional Systems and Signal Processing
SAR image reconstruction and autofocus by compressed sensing
Digital Signal Processing
X-Ray CT Image Reconstruction via Wavelet Frame Based Regularization and Radon Domain Inpainting
Journal of Scientific Computing
An Efficient Algorithm for l0 Minimization in Wavelet Frame Based Image Restoration
Journal of Scientific Computing
Framelet based pan-sharpening via a variational method
Neurocomputing
A coupled variational model for image denoising using a duality strategy and split Bregman
Multidimensional Systems and Signal Processing
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Real images usually have sparse approximations under some tight frame systems derived from framelets, an oversampled discrete (window) cosine, or a Fourier transform. In this paper, we propose a method for image deblurring in tight frame domains. It is reduced to finding a sparse solution of a system of linear equations whose coefficient matrix is rectangular. Then, a modified version of the linearized Bregman iteration proposed and analyzed in [J.-F. Cai, S. Osher, and Z. Shen, Math. Comp., to appear, UCLA CAM Report (08-52), 2008; J.-F. Cai, S. Osher, and Z. Shen, Math. Comp., to appear, UCLA CAM Report (08-06), 2008; S. Osher et al., UCLA CAM Report (08-37), 2008; W. Yin et al., SIAM J. Imaging Sci., 1 (2008), pp. 143-168] can be applied. Numerical examples show that the method is very simple to implement, robust to noise, and effective for image deblurring.