Department of Mathematics
Total variation structured total least squares method for image restoration
In this paper, we study the total variation structured total least squares method for image restoration. In the image restoration problem, the point spread function is corrupted by errors. In the model, we study the objective function by minimizing two variables: the restored image and the estimated error of the point spread function. The proposed objective function consists of the data-fitting term containing these two variables, the magnitude of error and the total variation regularization of the restored image. By making use of the structure of the objective function, an efficient alternating minimization scheme is developed to solve the proposed model. Numerical examples are also presented to demonstrate the effectiveness of the proposed model and the efficiency of the numerical scheme.Copyright © by SIAM.
Alternating minimization, Image restoration, Regularization, Structured total least squares, Total variation
Source Publication Title
SIAM Journal on Scientific Computing
Society for Industrial and Applied Mathematics
Link to Publisher's Edition
Zhao, Xi-Le, Wei Wang, Tie-Yong Zeng, Ting-Zhu Huang, and Michael K. Ng. "Total variation structured total least squares method for image restoration." SIAM Journal on Scientific Computing 35.6 (2013): B1304-B1320.