Department of Mathematics
A new convex optimization model for multiplicative noise and blur removal
The main contribution of this paper is to propose a new convex optimization model for multiplicative noise and blur removal. The main idea is to rewrite a blur and multiplicative noise equation such that both the image variable and the noise variable are decoupled. The resulting objective function involves the total variation regularization term, the term of variance of the inverse of noise, the l1-norm of the data-fitting term among the observed image, and noise and image variables. Such a convex minimization model can be solved efficiently by using many numerical methods in the literature. Numerical examples are presented to demonstrate the effectiveness of the proposed model. Experimental results show that the proposed model can handle blur and multiplicative noise (Gamma, Gaussian, or Rayleigh distribution) removal quite well. © 2014 Society for Industrial and Applied Mathematics.
Alternating direction method, Convex optimization, Image restoration, Multiplicative noise, Total variation
Source Publication Title
SIAM Journal on Imaging Sciences
Society for Industrial and Applied Mathematics
Link to Publisher's Edition
Zhao, Xi-Le, Fan Wang, and Michael K. Ng. "A new convex optimization model for multiplicative noise and blur removal." SIAM Journal on Imaging Sciences 7.1 (2014): 456-475.