Image Deblurring Via an Adaptive Dictionary Learning Strategy

Abstract: Recently, sparse representation has been applied to image deblurring. The dictionary is the fundamental part of it and the proper selection of dictionary is very important to achieve superperformance. The global learned dictionary might achieve inferior performances since it could not mine the specific informati n such as the texture and edge which is contained in the blurred image. However, it is a computational burden to train a new dictionary for image deblurring which requires the whole image (or most parts)as input; training the dictionary on only a few patches would result in over-fitting. To addressthe problem, we instead propose an online adaption strategy to transfer the global learned dictionary to a specific image. In our deblurring algorithm, the sparse coefficients, latent image, blur kernel and the dictionary are updated alternatively. And in every step, the global learned dictionary is updated in an online form via sampling only few training patches from the target noisy image. Since our adaptive dictionaryexploits the specific information, our deblurring algorithm shows superior performance over otherstate-ofthe-art algorithms.
Keywords: sparse representation, adaptive dictionary learning, image deblurring
Author: Lei Li, Ruiting Zhang
Journal Code: jptkomputergg140120

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