Department of Mathematics
Solving constrained total-variation image restoration and reconstruction problems via alternating direction methods
In this paper, we study alternating direction methods for solving constrained totalvariation image restoration and reconstruction problems. Alternating direction methods can be implementable variants of the classical augmented Lagrangian method for optimization problems with separable structures and linear constraints. The proposed framework allows us to solve problems of image restoration, impulse noise removal, inpainting, and image cartoon+texture decomposition. As the constrained model is employed, we need only to input the noise level, and the estimation of the regularization parameter is not required in these imaging problems. Experimental results for such imaging problems are presented to illustrate the effectiveness of the proposed method. We show that the alternating direction method is very efficient for solving image restoration and reconstruction problems. © 2010 Society for Industrial and Applied Mathematics.
Alternating direction method, Augmented Lagrangian, Image reconstruction, Image restoration, Total-variation
Source Publication Title
SIAM Journal on Scientific Computing
Society for Industrial and Applied Mathematics
Link to Publisher's Edition
Ng, Michael K., Pierre Weiss, and Xiaoming Yuan. "Solving constrained total-variation image restoration and reconstruction problems via alternating direction methods." SIAM Journal on Scientific Computing 32.5 (2010): 2710-2736.