一种基于双重网络模型的单幅图像超分辨率方法

doi:10.3969/j.issn.0255-8297.2021.02.014

Abstract

Abstract: This article mainly improves the efficient sub-pixel convolutional neural network (ESPCN) algorithm in the field of deep learning. By adding residual network knowledge and adjusting original ESPCN structure, a dual network model is proposed for single frame image super-resolution reconstruction method. Experimental results show that this algorithm can effectively improve the accuracy of single-image super-resolution reconstruction and enrich the detailed information after reconstruction.

Key words: residual network, sub-pixel convolution, band group normalization, hidden layer

CLC Number:

P315.69

NI Cui, WANG Peng, ZHANG Guangyuan, LI Kefeng. A Single Image Super-Resolution Method Based on the Dual Network Model[J]. Journal of Applied Sciences, 2021, 39(2): 321-329.

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A Single Image Super-Resolution Method Based on the Dual Network Model

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