TY - JOUR
AU - Zhang, Jun
AB -  Blind image deconvolution has attracted growing attention in image processing and computer vision. The total variation (TV) regularization can effectively preserve image edges. However, due to lack of self-adaptability, it does not perform very well on restoring images with complex structures. In this paper, we propose a new blind image deconvolution model using an adaptive weighted TV regularization. This model can better handle local features of image. Numerically, we design an effective alternating direction method of multipliers (ADMM) to solve this non-smooth model. Experimental results illustrate the superiority of the proposed method compared with other related blind deconvolution methods.
TI - Blind image deconvolution via an adaptive weighted TV regularization
JF - Journal of Intelligent and Fuzzy Systems
DO - 10.3233/jifs-223828
DA - 2023-04-03
UR - https://www.deepdyve.com/lp/ios-press/blind-image-deconvolution-via-an-adaptive-weighted-tv-regularization-nlg10CLoxN
SP - 6497
EP - 6511
VL - 44
IS - 4
DP - DeepDyve
ER -