Image reconstruction with locally adaptive sparsity and nonlocal robust regularization

Weisheng Dong, Guangming Shi, Xin Li, Lei Zhang, Xiaolin Wu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

74 Citations (Scopus)

Abstract

Sparse representation based modeling has been successfully used in many image-related inverse problems such as deblurring, super-resolution and compressive sensing. The heart of sparse representations lies on how to find a space (spanned by a dictionary of atoms) where the local image patch exhibits high sparsity and how to determine the image local sparsity. To identify the locally varying sparsity, it is necessary to locally adapt the dictionary learning process and the sparsity-regularization parameters. However, spatial adaptation alone runs into the risk of over-fitting the data because variation and invariance are two sides of the same coin. In this work, we propose two sets of complementary ideas for regularizing image reconstruction process: (1) the sparsity regularization parameters are locally estimated for each coefficient and updated along with adaptive learning of PCA-based dictionaries; (2) a nonlocal self-similarity constraint is introduced into the overall cost functional to improve the robustness of the model. An efficient alternative minimization algorithm is present to solve the proposed objective function and then an effective image reconstruction algorithm is presented. The experimental results on image deblurring, super-resolution and compressive sensing demonstrate that the proposed image reconstruct method outperforms many existing image reconstruction methods in both PSNR and visual quality assessment.

Original languageEnglish
Pages (from-to)1109-1122
Number of pages14
JournalSignal Processing: Image Communication
Volume27
Issue number10
DOIs
Publication statusPublished - 1 Nov 2012

Keywords

  • Image reconstruction
  • Local dictionary learning
  • Nonlocal regularization
  • Sparse representation

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering

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