A novel PSO-based parameter estimation for total variation regularization

In this paper a novel approach for estimation of regularization parameter in Total Variation (TV) method, based on Particle Swarm Optimization (PSO) is presented. As regards to the fact that this parameter has a great impact on how well the TV may work, many techniques have been used by researchers but mostly are somehow based on an assumption on the nature of the problem. This work suggests a new method as in which, the PSO itself learns how to deal with this parameter without any prior knowledge, just by tracking the procedure of how the changes of this parameter affect the performance of TV. Finally experimental results are presented to show performance of the proposed method in comparison to previous works.