Abstract
In this paper, we study a novel inverse filtering method by using a dictionary approach. The main idea is to combine a learned dictionary for the representation of the deconvoluted image and an inverse filter based on nonnegativity and support constraints, to deconvolute the observed image with an unknown point spread function. The advantage of this approach is that the target image can be represented with more details by learned basis in the dictionary. We also employ the alternating direction method of multipliers to solve the resulting optimization problem. Experimental results are presented to show that the performance of the proposed methods are better than other testing methods for several testing images.
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