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

  • Chunhong Liu
  • , Faming Fang
  • , Yingying Xu
  • , Chaomin Shen*
  • *此作品的通讯作者
  • East China Normal University

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

摘要

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.

源语言英语
主期刊名Trends in Artificial Intelligence - 14th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2016, Proceedings
编辑Richard Booth, Min-Ling Zhang
出版商Springer Verlag
页251-261
页数11
ISBN(印刷版)9783319429106
DOI
出版状态已出版 - 2016
活动14th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2016 - Phuket, 泰国
期限: 22 8月 2016 → 26 8月 2016

丛书

姓名Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
卷9810 LNCS
ISSN(印刷版)0302-9743
ISSN(电子版)1611-3349

会议

会议14th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2016
国家/地区泰国
市Phuket
时期22/08/16 → 26/08/16

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