An Adaptive Non-local Total Variation Blind Deconvolution Employing Split Bregman Iteration
Huijie Gao, Zhiyong Zuo · 2012
Total variation has been used as a popular and effective image prior model in regularization-based image blind restoration, because of its ability to preserve edges. However, as the total variation model favors a piecewise constant solution, the processing results in the flat regions of the image being poor, and it cannot automatically balance the processing strength between different spatial property regions in the image. in this paper, we propose an adaptive non-local total variation image blind restoration algorithm for deblurring a single image via non-local total variation operator, which make full use of the spatially information distributed in the different image regions, and an extended split Bregman iteration is proposed to address the joint minimization problem. Extensive experiments demonstrate the proposed approach produces results superior to most methods in both visual image quality and quantitative measures.