TY - JOUR T1 - The Convex Relaxation Method on Deconvolution Model with Multiplicative Noise AU - Yumei Huang, Michael Ng & Tieyong Zeng JO - Communications in Computational Physics VL - 4 SP - 1066 EP - 1092 PY - 2013 DA - 2013/08 SN - 13 DO - http://doi.org/10.4208/cicp.310811.090312a UR - https://global-sci.org/intro/article_detail/cicp/7264.html KW - AB -

In this paper, we consider variational approaches to handle the multiplicative noise removal and deblurring problem. Based on rather reasonable physical blurring-noisy assumptions, we derive a new variational model for this issue. After the study of the basic properties, we propose to approximate it by a convex relaxation model which is a balance between the previous non-convex model and a convex model. The relaxed model is solved by an alternating minimization approach. Numerical examples are presented to illustrate the effectiveness and efficiency of the proposed method.