Sep. 2016

Article Contents

Chinese Optics> 2016> 9(5): 532-539.

HE Yang, HUANG Wei, WANG Xin-hua, HAO Jian-kun. Super-resolution image reconstruction based on sparse threshold[J]. Chinese Optics, 2016, 9(5): 532-539. doi: 10.3788/CO.20160905.0532

Citation:

HE Yang, HUANG Wei, WANG Xin-hua, HAO Jian-kun. Super-resolution image reconstruction based on sparse threshold[J].Chinese Optics, 2016, 9(5): 532-539.doi:10.3788/CO.20160905.0532

HE Yang, HUANG Wei, WANG Xin-hua, HAO Jian-kun. Super-resolution image reconstruction based on sparse threshold[J]. Chinese Optics, 2016, 9(5): 532-539. doi: 10.3788/CO.20160905.0532

Citation:

HE Yang, HUANG Wei, WANG Xin-hua, HAO Jian-kun. Super-resolution image reconstruction based on sparse threshold[J].Chinese Optics, 2016, 9(5): 532-539.doi:10.3788/CO.20160905.0532

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Super-resolution image reconstruction based on sparse threshold

doi:10.3788/CO.20160905.0532

HE Yang^{1, 2,},
HUANG Wei^1,,,
WANG Xin-hua¹,
HAO Jian-kun^{1, 2}

1.
State Key Laboratory of Applied Optics, Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China
2.
University of Chinese Academy of Sciences, Beijing 100049, China

Funds:

Foundation Project of State Key Laboratory of Applied Optics of ChinaY4223FQ141

More Information

Corresponding author:E-mail:huangw@ciomp.ac.cn
Received Date:11 May 2016
Rev Recd Date:13 Jun 2016
Publish Date:01 Oct 2016

Abstract

Abstract

In order to solve the problem of the time consuming of the super-resolution reconstruction algorithm based on dictionary learning, a method of super-resolution image reconstruction based on sparse threshold model is proposed. First of all, the over-complete dictionary couple based on the theory of joint dictionary by method of sparse threshold is obtained. And then, the sparse representation of feature block image is represented by sparse threshold OMP algorithm. Then, the initial super-resolution image is reconstructed by the high resolution dictionary. Finally, the global optimization of the initial super-resolution image is improved by the modified iterative back projection algorithm, which can improve the quality of reconstructed image. The experimental results show that the average peak signal to noise ratio(PSNR) is 30.1dB; the average structure self-similarity(SSIM) is 0.9379; the average computation time is 10.2s. This method can improve not only the speed of super-resolution reconstruction, but also the quality of reconstructed high resolution images.
- super resolution,
- sparse threshold,
- dictionary learning,
- iterative inverse projection algorithm

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References (20)

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