Infrared Image Super-Resolution via Generative Adversarial Network with Gradient Penalty Loss-Reference-Cited by-同舟云学术

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Infrared Image Super-Resolution via Generative Adversarial Network with Gradient Penalty Loss

Published:2022-05-16 Issue: Volume: Page:
ISSN:
Container-title:2022 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)
language:
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Author:

Mei Jian Qiang¹,Wen Ding Xue¹,Zheng Dandan²,Page Tom³

Affiliation:

1. School of Electronic Engineering, Tianjin University of Technology and Education,Tianjin,China

2. School of Electrical and Information Engineering, Tianjin University,Tianjin,China

3. Faculty of Engineering & Design, Institute of Technology,Dept. Computing & Electronic Engineering,Sligo,Ireland

Funder

Research and Development

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9806445/9806446/09806485.pdf?arnumber=9806485

Reference32 articles.

1. Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network

2. Background-subtraction using contour-based fusion of thermal and visible imagery

3. Ieee otcbvs ws series bench, terravic research infrared database;miezianko,0

4. A deep journey into superresolution: A survey;anwar;CoRR,2019

5. Improved training of wasserstein gans;gulrajani;ArXiv e-prints,2017

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