Single image super-resolution based on nonlocal sparse and low-rank regularization

  • Chunhong Liu
  • , Faming Fang
  • , Yingying Xu
  • , Chaomin Shen*
  • *Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Scopus citations

Abstract

Image super resolution (SR) is an active research topic to obtain an high resolution (HR) image from the low resolution (LR) observation. Many results of existing methods may be corrupted by some artifacts. In this paper, we propose an SR reconstruction method for single image based on nonlocal sparse and low-rank regularization. We form a matrix for each patch with its vectorized similar patches to utilize the redundancy of similar patches in natural images. This matrix can be decomposed as the low rank component and sparse part, where the low rank component depictures the similarity and the sparse part depictures the fine differences and outliers. The SR result is achieved by the iterative method and corroborated by experimental results, showing that our method outperforms other prevalent methods.

Original languageEnglish
Title of host publicationTrends in Artificial Intelligence - 14th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2016, Proceedings
EditorsRichard Booth, Min-Ling Zhang
PublisherSpringer Verlag
Pages251-261
Number of pages11
ISBN (Print)9783319429106
DOIs
StatePublished - 2016
Event14th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2016 - Phuket, Thailand
Duration: 22 Aug 201626 Aug 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9810 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference14th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2016
Country/TerritoryThailand
CityPhuket
Period22/08/1626/08/16

Keywords

  • Low-rank
  • Nonlocal self-similarity
  • Sparsity
  • Super resolution

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