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[1] Xie Chao, Lu Xiaobo, Zeng Weili, et al. Single frame super-resolution reconstructionbased on sparse representation [J]. Journal of Southeast University (English Edition), 2016, 32 (2): 177-182. [doi:10.3969/j.issn.1003-7985.2016.02.008]
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Single frame super-resolution reconstructionbased on sparse representation()
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Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:
32
Issue:
2016 2
Page:
177-182
Research Field:
Computer Science and Engineering
Publishing date:
2016-06-20

Info

Title:
Single frame super-resolution reconstructionbased on sparse representation
Author(s):
Xie Chao1;  2;  Lu Xiaobo1;  2;  Zeng Weili3
1School of Automation, Southeast University, Nanjing 210096, China
2Key Laboratory of Measurement and Control of Complex Systems of Engineering of Ministry of Education, Southeast University, Nanjing 210096, China
3College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
Keywords:
single frame super-resolution reconstruction;  sparse representation;  local orientation estimation;  principal component analysis(PCA);  consistency of gradients
PACS:
TP391
DOI:
10.3969/j.issn.1003-7985.2016.02.008
Abstract:
In order to effectively improve the quality of recovered images, a single frame super-resolution reconstruction method based on sparse representation is proposed. The combination method of local orientation estimation-based image patch clustering and principal component analysis is used to obtain a series of geometric dictionaries of different orientations in the dictionary learning process. Subsequently, the dictionary of the nearest orientation is adaptively assigned to each of the input patches that need to be represented in the sparse coding process. Moreover, the consistency of gradients is further incorporated into the basic framework to make more substantial progress in preserving more fine edges and producing sharper results. Two groups of experiments on different types of natural images indicate that the proposed method outperforms some state-of-the-art counterparts in terms of both numerical indicators and visual quality.

References:

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Memo

Memo:
Biographies: Xie Chao(1987—), male, graduate; Lu Xiaobo(corresponding author), male, doctor, professor, xblu2013@126.com.
Foundation items: The National Natural Science Foundation of China(No.61374194, No.61403081), the National Key Science & Technology Pillar Program of China(No.2014BAG01B03), the Natural Science Foundation of Jiangsu Province(No.BK20140638), the Priority Academic Program Development of Jiangsu Higher Education Institutions.
Citation: Xie Chao, Lu Xiaobo, Zeng Weili.Single frame super-resolution reconstruction based on sparse representation[J].Journal of Southeast University(English Edition), 2016, 32(2):177-182.doi:10.3969/j.issn.1003-7985.2016.02.008.
Last Update: 2016-06-20