基于递归多尺度卷积网络的图像超分辨率重建

doi:10.16451/j.cnki.issn1003-6059.202011002

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Abstract The performance of image super-resolution reconstruction networks is improved by deepening the depth.However,deepening the network makes the number of parameters increase rapidly,and thus it is hard to train the network and store the memory.To reduce the scale of the deep network and keep its reconstruction performance as much as possible,a concise recursive multi-scale convolutional network is proposed for super-resolution reconstruction based on the concepts of recursion and multi-scale.Firstly,the multi-scale module is employed to extract the features of the image with different scales.Then,the network is deepened by the recursive operation without increasing the number of network parameters.Finally,the outputs of each recursive operation are fused as the input for the reconstruction part.Experimental results show that the network parameters of the proposed method are fewer than those of some existing super-resolution methods with better reconstruction results.

Key words： Super-Resolution Reconstruction Deep Learning Recursion Multi-scale

Received: 31 July 2020

ZTFLH:

TP391

Corresponding Authors: ZHAO Jianwei,Ph.D,professor.Her research interests include intelligent computing and image processing.

About author:: GAO Qingqing,master student.Her research interests include intelligent computing and image processing;ZHOU Zhenghua,Ph.D,associate profe-ssor.His research interests include intelligent computing and image processing.

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Cite this article:

GAO Qingqing,ZHAO Jianwei,ZHOU Zhenghua. Image Super-Resolution Reconstruction Based on Recursive Multi-scale Convolutional Networks[J]. , 2020, 33(11): 972-980.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.202011002 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2020/V33/I11/972

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