A new degradation model and an improved SRGAN for multi-image super-resolution reconstruction-Reference-Cited by-同舟云学术

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A new degradation model and an improved SRGAN for multi-image super-resolution reconstruction

Published:2024-03-25 Issue: Volume: Page:1-20
ISSN:1368-2199
Container-title:The Imaging Science Journal
language:en
Short-container-title:The Imaging Science Journal

Author:

Li Hongan¹²^ORCID,Cheng Lizhi¹^ORCID,Liu Jun³

Affiliation:

1. College of Computer Science and Technology, Xi'an University of Science and Technology, Xi'an, People's Republic of China

2. State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, People's Republic of China

3. Shaanxi Technical College of Finance and Economics, Xian yang, People's Republic of China

Funder

the Natural Science Basis Research Plan in Shaanxi Province of China

the Open Project Program of State Key Laboratory of Virtual Reality Technology and Systems Beihang University

the high-level talent introduction project of Shaanxi Technical College of Finance & Economics

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/13682199.2024.2331813

Reference45 articles.

1. Guided filter‐based multi‐scale super‐resolution reconstruction

2. Deep Back-ProjectiNetworks for Single Image Super-Resolution

3. An Image Super-resolution Reconstruction Method by Using of Deep Learning

4. Super-Resolution Reconstruction Method of Remote Sensing Image Based on Multi-Feature Fusion

5. A Review of the Deep Learning Methods for Medical Images Super Resolution Problems

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Hybrid Thresholding for Image Deconvolution in Expectation Maximization framework;The Imaging Science Journal;2024-05-09

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