Abstract

A semi-blind image restoration algorithm is proposed based on reduced non-convex approximation of Luminita Vese and Tony Chan's (C-V) denoising model. Compared with C-V denoising model, we modify the fidelity term and add a term on point spread function (PSF). The function depends on two variables: the image function to be restored <i>u</i> and the standard deviation of Gaussian kernel to be estimated <i>σ</i>. Then the problems consist in solving a system with two coupled equations. Compared with the Leah Bar's semi-blind image restoration model which must solve three coupled equations, our method only needs to solve two equations. Furthermore, the estimation of <i>f</i> by our algorithm is superior to Leah Bar's algorithm. The experimental results demonstrate that the proposed method is effective.

© 2008 Chinese Optics Letters

Model-based restoration using light vein for range-gated imaging systems

Canjin Wang, Tao Sun, Tingfeng Wang, Rui Wang, Jin Guo, and Yuzhen Tian
Appl. Opt. 55(26) 7229-7235 (2016)

Deep denoiser prior based deep analytic network for lensless image restoration

Hao Zhou, Huajun Feng, Wenbin Xu, Zhihai Xu, Qi Li, and Yueting Chen
Opt. Express 29(17) 27237-27253 (2021)

Satellite image restoration in the context of a spatially varying point spread function

Nasreddine Hajlaoui, Caroline Chaux, Guillaume Perrin, Frédéric Falzon, and Amel Benazza-Benyahia
J. Opt. Soc. Am. A 27(6) 1473-1481 (2010)

Semi-supervised generative adversarial learning for denoising adaptive optics retinal images

Shidan Wang, Kaiwen Li, Qi Yin, Ji Ren, and Jie Zhang
Biomed. Opt. Express 15(3) 1437-1452 (2024)

Fast and optimal multiframe blind deconvolution algorithm for high-resolution ground-based imaging of space objects

Charles L. Matson, Kathy Borelli, Stuart Jefferies, Charles C. Beckner, Jr., E. Keith Hege, and Michael Lloyd-Hart
Appl. Opt. 48(1) A75-A92 (2009)