Terahertz image super-resolution based on a deep convolutional neural network

被引：64

作者：

Long, Zhenyu ^{[1
]}

Wang, Tianyi ^{[1
]}

You, Chengwu ^{[1
]}

Yang, Zhengang ^{[1
]}

Wang, Kejia ^{[1
]}

Liu, Jinsong ^{[1
]}

机构：

[1] Huazhong Univ Sci & Technol, Sch Opt & Elect Informat, Wuhan Natl Lab Optoelect, Wuhan 430074, Hubei, Peoples R China

来源：

APPLIED OPTICS | 2019年 / 58卷 / 10期

基金：

中国国家自然科学基金;

关键词：

30;

D O I：

10.1364/AO.58.002731

中图分类号：

O43 [光学];

学科分类号：

070207 ; 0803 ;

摘要：

We propose an effective and robust method for terahertz (THz) image super-resolution based on a deep convolutional neural network (CNN). A deep CNN model is designed. It learns an end-to-end mapping between the low- and high-resolution images. Blur kernels with multiple width and noise with multiple levels are taken into the training set so that the network can handle THz images very well. Quantitative comparison of the proposed method and other super-resolution methods on the synthetic THz images indicates that the proposed method performs better than other methods in accuracy and visual improvements. Experimental results on real THz images show that the proposed method significantly improves the quality of THz images with increased resolution and decreased noise, which proves the practicability and exactitude of the proposed method. (C) 2019 Optical Society of America

引用

页码：2731 / 2735

页数：5

共 30 条

[1] NTIRE 2017 Challenge on Single Image Super-Resolution: Dataset and Study [J].