Technology of Graphic & Image
|
1555-1559

Compressed sensing and similarity constraint image super-resolution

Wu Keyong1
Chen Dong2
Xin Ning2
Cao Guixing2
1. State Key Laboratory of Integrated Services Networks, Xidian University, Xi'an 710071, China
2. Institute of Telecommunication Satellite, China Academy of Space Technology, Beijing 100094, China

Abstract

Aiming at the problem of poor quality and contrived details in super-resolution reconstruction by external learning, this paper proposed a single image super-resolution based on compressed sensing and similarity constraint. Firstly, this paper proposed a classified dictionary based on measurement domain by using the linear relationship between the measurement domain and the frequency domain in compressed sensing, which improved the representation of dictionaries, then, it used the non-local similarity in the reconstruction process, combined the sparsity under the feature dictionary and similar block information as priori information to regularize super-resolution reconstruction, and finally recovered the high resolution image. Experimental results show that the reconstructed image has rich details and defined edges, and the subjective quality is good.

Foundation Support

国家自然科学基金资助项目(61372068,61772387)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2017.12.0833
Publish at: Application Research of Computers Printed Article, Vol. 36, 2019 No. 5
Section: Technology of Graphic & Image
Pages: 1555-1559
Serial Number: 1001-3695(2019)05-057-1555-05

Publish History

[2019-05-05] Printed Article

Cite This Article

吴科永, 陈东, 辛宁, 等. 压缩感知和相似性约束的图像超分辨率重构算法 [J]. 计算机应用研究, 2019, 36 (5): 1555-1559. (Wu Keyong, Chen Dong, Xin Ning, et al. Compressed sensing and similarity constraint image super-resolution [J]. Application Research of Computers, 2019, 36 (5): 1555-1559. )

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  • Application Research of Computers Monthly Journal
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Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

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