Deblurring Algorithm Using Alternating Low Rank Augmented Lagrangian with Iterative Priors

Abstract

The paper focuses on the Enhanced Augmented Lagrangian method with sparse regularization for image deblurring. The method suggested by ALTERNATING LOW RANK AUGMENTED LAGRANGIAN WITH ITERATIVE A PRIOR is novel in the following ways. (i) Faster convergence leading to speeder execution through rank regulations (ii) using derivatives and low rank together as regularization priors (iii) penalty and regularization weights ensure that each iteration hits a global minimum with a steep descent. The proposed method begins with the lowest rank matrix, which is the sparsest matrix available. The final deblurred result is very successful in achieving good dB improvements through rank regulation.