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Image super-resolution based on multi-space sparse representation

Authors:

Baocai YinAuthors Info & Claims

ICIMCS '10: Proceedings of the Second International Conference on Internet Multimedia Computing and Service

Pages 11 - 14

https://doi.org/10.1145/1937728.1937731

Published: 30 December 2010 Publication History

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Abstract

Sparse representation provides a new method of generating a super-resolution image from a single low resolution input image. An over-complete base for sparse representation is an essential part of such methods. However discovering the over-complete base with efficient representation from a large amount of image patches is a difficult problem. We make efforts in sparse representation and its implementation to solve the problem. In the representation, image patches are decomposed into two structure and texture components represented by the over-complete bases of their own spaces so that their high-level features can be captured by the bases. In the implementation, a prior knowledge about low resolution images generation is combined to the typical base construction for high construction quality. Finally a super-resolution construction based on multi-space sparse representation is proposed. Experiment results demonstrate that the proposed method significantly improve the PSNR and visual quality of reconstructed high-resolution image.

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Cited By

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Wang KHu HLi LLiu T(2019)Discriminative Face Recognition Methods with Structure and Label Information via $$l_2$$ l 2 -Norm RegularizationNeural Processing Letters10.1007/s11063-019-10106-9Online publication date: 28-Aug-2019
https://doi.org/10.1007/s11063-019-10106-9
Zheng WDeng FMo SJin XQu YZhou JZou RShuai JXie ZLong SZheng C(2018)Image super-resolution reconstruction algorithm based on Bayesian theory2018 13th IEEE Conference on Industrial Electronics and Applications (ICIEA)10.1109/ICIEA.2018.8398025(1934-1938)Online publication date: May-2018
https://doi.org/10.1109/ICIEA.2018.8398025
Dai SZhan YMao QZhang S(2013)A Video Semantic Analysis Method Based on Kernel Discriminative Sparse Representation and Weighted KNNProceedings of the 2013 IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, Physical and Social Computing10.1109/GreenCom-iThings-CPSCom.2013.154(879-886)Online publication date: 20-Aug-2013
https://dl.acm.org/doi/10.1109/GreenCom-iThings-CPSCom.2013.154

Index Terms

Image super-resolution based on multi-space sparse representation
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision

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Information

Published In

ICIMCS '10: Proceedings of the Second International Conference on Internet Multimedia Computing and Service

December 2010

218 pages

ISBN:9781450304603

DOI:10.1145/1937728

General Chairs:
Yong Rui
Microsoft China, China
,
Klara Nahrstedt
University of Illinois at Urbana-Champaign
,
Xiaofei Xu
Harbin Institute of Technology, China
,
Program Chairs:
Hongxun Yao
Harbin Institute of Technology, China
,
Shuqiang Jiang
Institute of Computing Technology, Chinese Academy of Science, China
,
Jian Cheng
Institute of Automation, Chinese Academy of Science, China

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 30 December 2010

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Natural Science Foundation of Beijing Municipality
National Natural Science Foundation of China
National Key Technology R&D Program

Conference

ICIMCS '10

ICIMCS '10: The Second International Conference on Internet Multimedia Computing and Service

December 30 - 31, 2010

Harbin, China

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Overall Acceptance Rate 163 of 456 submissions, 36%

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Cited By

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Wang KHu HLi LLiu T(2019)Discriminative Face Recognition Methods with Structure and Label Information via $$l_2$$ l 2 -Norm RegularizationNeural Processing Letters10.1007/s11063-019-10106-9Online publication date: 28-Aug-2019
https://doi.org/10.1007/s11063-019-10106-9
Zheng WDeng FMo SJin XQu YZhou JZou RShuai JXie ZLong SZheng C(2018)Image super-resolution reconstruction algorithm based on Bayesian theory2018 13th IEEE Conference on Industrial Electronics and Applications (ICIEA)10.1109/ICIEA.2018.8398025(1934-1938)Online publication date: May-2018
https://doi.org/10.1109/ICIEA.2018.8398025
Dai SZhan YMao QZhang S(2013)A Video Semantic Analysis Method Based on Kernel Discriminative Sparse Representation and Weighted KNNProceedings of the 2013 IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, Physical and Social Computing10.1109/GreenCom-iThings-CPSCom.2013.154(879-886)Online publication date: 20-Aug-2013
https://dl.acm.org/doi/10.1109/GreenCom-iThings-CPSCom.2013.154

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Image super-resolution based on multi-space sparse representation

Super-resolution of hyperspectral image via superpixel-based sparse representation

Hyperspectral image super-resolution through clustering-based sparse representation