PROJECT TITLE :

An efficient sparse optimization algorithm for weighted '0 shearlet-based method for image deblurring - 2017

ABSTRACT:

Sparsity is one in every of the key ideas that enables the signal recovery at a considerably lower subsample rate than required by the Nyquist-Shannon sampling theorem. By employing a multistate rework, like wavelets and shearlets system, the sparse representation of signals will be obtained. To more exploit the sparsity of the reconstructed signal, a generalized gradient regularizer is introduced to the proposed model. Motivated by the idea of iterative support detection (ISD), an optimization algorithm framework for image deblurring is given. The algorithm aims to resolve a reweighted l0-minimization downside in split Bregman framework, and therefore the weights used for the following iteration are set by an ISD method. The advantage of this process is that it forms an iterative-feedback mechanism, which improves the effectiveness for solution looking. A series of experiments are presented to demonstrate the provision of the proposed framework. Experimental results show that this methodology yields vital improvement in peak signal-to-noise ratio in comparison to different counterparts. But, the numerical experiments also show that a lot of computing time is needed because of the use of the redundant multiscale system.