Image deblurring and super-resolution by adaptive sparse domain selection and adaptive regularization

Weisheng Dong, Lei Zhang, Guangming Shi, Xiaolin Wu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

1112 Citations (Scopus)


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 l1-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.
Original languageEnglish
Article number5701777
Pages (from-to)1838-1857
Number of pages20
JournalIEEE Transactions on Image Processing
Issue number7
Publication statusPublished - 1 Jul 2011


  • Deblurring
  • image restoration (IR)
  • regularization
  • sparse representation
  • super-resolution

ASJC Scopus subject areas

  • Software
  • Computer Graphics and Computer-Aided Design


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