Image Super-resolution Based on Alternate K-Singular Value Decomposition

XU Jian; CHANG Zhiguo; ZHANG Xiaodan

doi:10.13203/j.whugis20150095

Volume 42 Issue 8

Aug. 2017

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Geomatics and Information Science of Wuhan University > 2017 > 42(8): 1137-1143. > DOI: 10.13203/j.whugis20150095

XU Jian, CHANG Zhiguo, ZHANG Xiaodan. Image Super-resolution Based on Alternate K-Singular Value Decomposition[J]. Geomatics and Information Science of Wuhan University, 2017, 42(8): 1137-1143. DOI: 10.13203/j.whugis20150095

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Image Super-resolution Based on Alternate K-Singular Value Decomposition

1.
School of Telecommunication and Information Engineering, Xi'an University of Posts and Telecommunications, Xi'an 710121, China
2.
School of Information Engineering, Chang'an University, Xi'an 710064, China

Funds:

The National Natural Science Foundation of China 61601362

The National Natural Science Foundation of China 61571361

The National Natural Science Foundation of China 61671377

The National Natural Science Foundation of China 41504115

Shaanxi International Cooperation and Exchange Plan 2015KW-005

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Author Bio:
XU Jian, PhD, associated professor, specializes in image super-resolution. E-mail: xujian_paper@163.com
Corresponding author:
CHANG Zhiguo, PhD, associated professor. E-mail: chang-zg@126.com
Received Date: August 17, 2016
Published Date: August 04, 2017

Graphical Abstract

Abstract

Abstract

The coupled dictionary training algorithm in super-resolution based on sparse representation are directly related to the detail recovery capability of the algorithm, but the existing algorithm makes the dictionaries lack texture structure information. This paper proposes an alternate K-singular value decomposition dictionary training algorithm. This algorithm is composed of a training stage and a testing stage. In the training stage, the best low rank approximations of low and high frequency patches are used for the updating of the dictionaries. This method makes the sparse representations of low and high frequency patches becomes more and more similar with the increasing of the iteration number. In the testing stages, the high frequency details can be estimated by multiplying the sparse representations generated with low frequency patches with the high frequency dictionary. The experimental results demonstrate that the proposed algorithm can provide clearer resultant images. Compared with many existing methods, the average peak signal to noise ratio exceeds about 0.3dB and structure similarity exceeds about 0.1.
- super-resolution,
- sparse representation,
- dictionary training,
- singular value decomposition

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