An Efficient Augmented Lagrangian Method for Impulse Noise Removal via Learned Dictionary

摘要

This paper presents a novel augmented Lagrangian method for impulse noise removal via learned dictionary. We reformulate the L1-L1 minimization into an augmented Lagrangian scheme through adding a new auxiliary variable, additionally the dictionary is updated by simply adding the multiplication of dual and primal variables. Experimental results demonstrate that the new proposed method can obtain very significantly superior performance than the current state-of-the-art variational methods for salt-and-pepper noise removal.