Bound Alternative Direction Optimization for Image Deblurring.

This paper proposes a new method, bound alternative direction method (BADM), to address the l p (p ∊ (0, 1)) minimization problems in image deblurring. The approach is to first obtain a bound unconstrained problem through bounding the l p regularizer by a novel majorizer and then, based on a variable splitting, to reformulate the bound unconstrained problem into a constrained one, which is then addressed via an augmented Lagrangian method. The proposed algorithm actually combines the reweighted l 1 minimization method and the alternating direction method of multiples (ADMM) such that it succeeds in extending the application of ADMM to l p minimization problems. The conducted experimental studies demonstrate the superiority of the proposed algorithm for the synthesis l 1 minimization over the state-of-the-art algorithms for the synthesis l 1 minimization on image deblurring. [ABSTRACT FROM AUTHOR]