基于双并行残差网络的遥感图像超分辨率重建

doi:10.16451/j.cnki.issn1003-6059.202108009

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Abstract The image super-resolution reconstruction algorithm generates a poor effect for the remote sensing images due to different sizes of ground objects and high complexity in the images. Aiming at this problem, a dual-parallel lightweight residual attention network is proposed to increase the reconstruction result. Firstly, a multi-scale shallow feature extraction block(MFEB) is put forward to gain the feature information of different receptive field sizes. The problem of the ground objects with different sizes can be solved by MFEB. Secondly, a lightweight residual attention block(LRAB) is designed with asymmetric convolution and attention mechanism. And thus, the model parameters are reduced and more high-frequency information is captured. Then, the parallel network with different convolution kernels is designed to fuse different receptive fields. Besides, lots of skip connections are employed in residual blocks to increase the reusability of information. Finally, experiments show that the proposed model produces superior performance.

Key words： Remote Sensing Image Super-Resolution Reconstruction Parallel Network Lightweight Asymmetric Convolution

Received: 06 May 2021

ZTFLH:

TP 391

Fund:National Natural Science Foundation of China(No.61703278)

Corresponding Authors: LIU Cong, Ph.D., associate professor. His research interests include computing intelligence, pattern recognition and image processing.

About author:: WANG Yaxin, master student. Her research interests include image super resolution reconstruction.

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Cite this article:

LIU Cong,WANG Yaxin. Remote Sensing Image Super-Resolution Reconstruction Based on Dual-Parallel Residual Network[J]. , 2021, 34(8): 760-767.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.202108009 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2021/V34/I8/760

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