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

应用科学学报 ›› 2021, Vol. 39 ›› Issue (2): 321-329.doi: 10.3969/j.issn.0255-8297.2021.02.014

• 信号与信息处理 • 上一篇

一种基于双重网络模型的单幅图像超分辨率方法

倪翠, 王朋, 张广渊, 李克峰

山东交通学院信息科学与电气工程学院, 山东济南 250357

收稿日期:2020-10-23 发布日期:2021-04-01
通信作者: 倪翠,副教授,研究方向为数字图像处理。E-mail:emilync@126.com E-mail:emilync@126.com
基金资助:
国家自然科学基金青年基金（No.61502277）资助

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

NI Cui, WANG Peng, ZHANG Guangyuan, LI Kefeng

School of Information Science and Electric Engineering, Shandong JiaoTong University, Jinan 250357, Shandong, China

Received:2020-10-23 Published:2021-04-01

摘要/Abstract

摘要： 本文对深度学习领域中的高效亚像素卷积神经网络（efficient sub-pixel convolutional neural network，ESPCN）算法进行了改进，通过加入残差网络知识，调整原有的ESPCN构造结构，提出了一种双重网络模型下单幅图像超分辨率重建方法。通过实验证明：该算法能够有效地提高单幅图像超分辨率重建的精度，丰富重建后的细节信息。

关键词: 残差网络, 亚像素卷积, 带组归一化, 隐藏层

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

中图分类号:

P315.69

倪翠, 王朋, 张广渊, 李克峰. 一种基于双重网络模型的单幅图像超分辨率方法[J]. 应用科学学报, 2021, 39(2): 321-329.

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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一种基于双重网络模型的单幅图像超分辨率方法

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

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