## Abstract

We consider and study total variation (TV) image restoration. In the literature there are several regularization parameter selection methods for Tikhonov regularization problems (e.g., the discrepancy principle and the generalized cross-validation method). However, to our knowledge, these selection methods have not been applied to TV regularization problems. The main aim of this paper is to develop a fast TV image restoration method with an automatic selection of the regularization parameter scheme to restore blurred and noisy images. The method exploits the generalized cross-validation (GCV) technique to determine inexpensively how much regularization to use in each restoration step. By updating the regularization parameter in each iteration, the restored image can be obtained. Our experimental results for testing different kinds of noise show that the visual quality and SNRs of images restored by the proposed method is promising. We also demonstrate that the method is efficient, as it can restore images of size $256\times 256$ in $\approx 20\text{\hspace{0.17em}}\mathrm{s}$ in the MATLAB computing environment.

© 2009 Optical Society of America

Full Article | PDF Article**OSA Recommended Articles**

Xiongjun Zhang, Bahram Javidi, and Michael K. Ng

Appl. Opt. **56**(9) D47-D51 (2017)

Tao He, Yasheng Sun, Jin Qi, Jie Hu, and Haiqing Huang

Appl. Opt. **58**(14) 3754-3766 (2019)

Tao He, Jie Hu, and Haiqing Huang

Appl. Opt. **57**(35) 10243-10256 (2018)