Semantic-Aware Guided Low-Light Image Super-Resolution-Reference-Cited by-同舟云学术

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Semantic-Aware Guided Low-Light Image Super-Resolution

Published:2024 Issue: Volume:12 Page:72408-72419
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Ren Sheng¹^ORCID,Cao Rui²^ORCID,Tan Wenxue¹,Tang Yayuan³

Affiliation:

1. School of Computer and Electrical Engineering, Hunan University of Arts and Science, Changde, China

2. Information and Network Center, Central South University, Changsha, China

3. School of Information Engineering, Hunan University of Science and Engineering, Yongzhou, China

Funder

National Natural Science Foundation of China

National Science Foundation of Hunan Province

Scientific Research Project of Hunan Provincial Department of Education

Hunan University of Arts and Sciences Doctoral Research Initiation Project

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6287639/10380310/10535111.pdf?arnumber=10535111

Reference60 articles.

1. Learning a Deep Convolutional Network for Image Super-Resolution

2. Accelerating the Super-Resolution Convolutional Neural Network

3. Accurate Image Super-Resolution Using Very Deep Convolutional Networks

4. Deeply-Recursive Convolutional Network for Image Super-Resolution

5. Structure-Preserving Super Resolution With Gradient Guidance

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