Context-aware single image super-resolution using locality-constrained group sparse representation

Chih Yun Tsai*, De An Huang, Min Chun Yang, Li Wei Kang, Yu Chiang Frank Wang

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

11 Citations (Scopus)

Abstract

We present a novel learning-based method for single image super-resolution (SR). Given a single input low-resolution (LR) image (and its image pyramid), we propose to learn context-specific image sparse representation, which aims at modeling the relationship between low and high-resolution image patch pairs of different context categories in terms of the learned dictionaries. To predict the SR image, we derive the context-specific sparse representation of each image patch in the LR input with additional locality and group sparsity constraints. While the locality constraint searches for the most similar image patches and uses the corresponding highresolution outputs for SR, the group sparsity constraint allows us to utilize the information from most relevant context categories for predicting the final SR output. Experimental results show the proposed method is able to quantitatively and qualitatively achieve state-of-the-art performance.

Original languageEnglish
Title of host publication2012 IEEE Visual Communications and Image Processing, VCIP 2012
DOIs
Publication statusPublished - 2012
Externally publishedYes
Event2012 IEEE Visual Communications and Image Processing, VCIP 2012 - San Diego, CA, United States
Duration: 2012 Nov 272012 Nov 30

Publication series

Name2012 IEEE Visual Communications and Image Processing, VCIP 2012

Conference

Conference2012 IEEE Visual Communications and Image Processing, VCIP 2012
Country/TerritoryUnited States
CitySan Diego, CA
Period2012/11/272012/11/30

Keywords

  • data locality
  • group Lasso
  • self-learning
  • sparse representation
  • Super-resolution

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

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