IEEE Trans Image Process - Image deblurring and super-resolution by adaptive sparse domain selection and adaptive regularization.

Tópicos

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{ detect(2391) sensit(1101) algorithm(908) }

Resumo

As a powerful statistical image modeling technique, sparse representation has been successfully used in various image restoration applications. The success of sparse representation owes to the development of the l(1)-norm optimization techniques and the fact that natural images are intrinsically sparse in some domains. The image restoration quality largely depends on whether the employed sparse domain can represent well the underlying image. Considering that the contents can vary significantly across different images or different patches in a single image, we propose to learn various sets of bases from a precollected dataset of example image patches, and then, for a given patch to be processed, one set of bases are adaptively selected to characterize the local sparse domain. We further introduce two adaptive regularization terms into the sparse representation framework. First, a set of autoregressive (AR) models are learned from the dataset of example image patches. The best fitted AR models to a given patch are adaptively selected to regularize the image local structures. Second, the image nonlocal self-similarity is introduced as another regularization term. In addition, the sparsity regularization parameter is adaptively estimated for better image restoration performance. Extensive experiments on image deblurring and super-resolution validate that by using adaptive sparse domain selection and adaptive regularization, the proposed method achieves much better results than many state-of-the-art algorithms in terms of both PSNR and visual perception.

Resumo Limpo

power statist imag model techniqu spars represent success use various imag restor applic success spars represent owe develop lnorm optim techniqu fact natur imag intrins spars domain imag restor qualiti larg depend whether employ spars domain can repres well under imag consid content can vari signific across differ imag differ patch singl imag propos learn various set base precollect dataset exampl imag patch given patch process one set base adapt select character local spars domain introduc two adapt regular term spars represent framework first set autoregress ar model learn dataset exampl imag patch best fit ar model given patch adapt select regular imag local structur second imag nonloc selfsimilar introduc anoth regular term addit sparsiti regular paramet adapt estim better imag restor perform extens experi imag deblur superresolut valid use adapt spars domain select adapt regular propos method achiev much better result mani stateoftheart algorithm term psnr visual percept

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