Super-resolution with nonlocal regularized sparse representation

Weisheng Dong, Guangming Shi, Lei Zhang, Xiaolin Wu

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

39 Citations (Scopus)


The reconstruction of a high resolution (HR) image from its low resolution (LR) counterpart is a challenging problem. The recently developed sparse representation (SR) techniques provide new solutions to this inverse problem by introducing the ℓ1-norm sparsity prior into the super-resolution reconstruction process. In this paper, we present a new SR based image super-resolution by optimizing the objective function under an adaptive sparse domain and with the nonlocal regularization of the HR images. The adaptive sparse domain is estimated by applying principal component analysis to the grouped nonlocal similar image patches. The proposed objective function with nonlocal regularization can be efficiently solved by an iterative shrinkage algorithm. The experiments on natural images show that the proposed method can reconstruct HR images with sharp edges from degraded LR images.
Original languageEnglish
Title of host publicationVisual Communications and Image Processing 2010
Publication statusPublished - 10 Dec 2010
EventVisual Communications and Image Processing 2010 - Huangshan, China
Duration: 11 Jul 201014 Jul 2010


ConferenceVisual Communications and Image Processing 2010


  • Iterative shrinkage algorithm
  • Nonlocal self-similarity
  • Sparse representation
  • Super-resolution

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering


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