基于多残差网络的结构保持超分辨重建

doi:10.16451/j.cnki.issn1003-6059.202103005

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Abstract Aiming at the problems of geometric structure distortion and missing details in image super-resolution reconstruction, a structure-preserving super-resolution reconstruction algorithm based on multi-residual network is proposed. Deep learning is carried out in the wavelet transform domain and the gradient domain. Three kinds of residual networks are introduced. The structure and the edge information are reconstructed by the residual gradient network. The high-frequency information of the image is reconstructed as a whole by the residual wavelet transform network. The network attention is adjusted by the residual channel attention network , the important channel features are emphatically learned, and the high frequency information of the image is recovered locally. Experiments show that the proposed algorithm achieves better performance in both quantitative results and visual effects.

Key words： Super-resolution Reconstruction Deep Learning Multi-residual Networks Structure Preservation

Received: 25 September 2020

ZTFLH:

TP 391.4

Fund:Youth Fund of National Natural Science Foundation of China(No.61902293), Young Talent Fund of University Association for Science and Technology in Shaanxi(No.20200103), Open Research Fund of CAS Key Laboratory of Spectral Imaging Technology(No.LSIT201901W), Fundamental Research Funds for the Central Universities(No.XJS200112)

Corresponding Authors: GAO Xinbo, Ph.D., professor. His research interests include pa-ttern recognition and computer vision.

About author:: ZHANG Mingjin, Ph.D., associate professor. Her research interests include pattern recognition and computer vision.PENG Xiaoqi, master student. Her research interests include pattern recognition and computer vision.GUO Jie, Ph.D., associate professor. His research interests include image transmission and processing.LI Yunsong, Ph.D., professor. His research interests include image transmission and processing.WANG Nannan, Ph.D., professor. His research interests include pattern recognition and computer vision.

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	ZHANG Mingjin
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Cite this article:

ZHANG Mingjin,PENG Xiaoqi,GUO Jie等. Structure-Preserving Super-Resolution Reconstruction Based on Multi-residual Network[J]. , 2021, 34(3): 232-240.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.202103005 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2021/V34/I3/232

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