Learning a Coordinated Network for Detail-Refinement Multiexposure Image Fusion-Reference-Cited by-同舟云学术

Learning a Coordinated Network for Detail-Refinement Multiexposure Image Fusion

Published:2023-02 Issue:2 Volume:33 Page:713-727
ISSN:1051-8215
Container-title:IEEE Transactions on Circuits and Systems for Video Technology
language:
Short-container-title:IEEE Trans. Circuits Syst. Video Technol.

Author:

Li Jiawei¹,Liu Jinyuan²^ORCID,Zhou Shihua¹^ORCID,Zhang Qiang³,Kasabov Nikola K.⁴^ORCID

Affiliation:

1. Key Laboratory of Advanced Design and Intelligent Computing, Ministry of Education, School of Software Engineering, Dalian University, Dalian, China

2. School of Software Technology, Dalian University of Technology, Dalian, China

3. School of Computer Science and Technology, Dalian University of Technology, Dalian, China

4. Knowledge Engineering and Discovery Research Institute, Auckland University of Technology, Auckland, New Zealand

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Program for Changjiang Scholars and Innovative Research Team in University

Program for Liaoning Innovative Research Team in University

Natural Science Foundation of Liaoning Province

High-Level Talent Innovation Support Program of Dalian City

Dalian Outstanding Young Science and Technology Talent Support Program

Scientific Research Fund of Liaoning Provincial Education Department

Dalian University Scientific Research Platform Program

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Media Technology

Link

http://xplorestaging.ieee.org/ielx7/76/10036154/09869621.pdf?arnumber=9869621

Reference77 articles.

1. Exploiting patch-based correlation for ghost removal in exposure fusion

2. CBAM: Convolutional block attention module;woo;Proc Eur Conf Comput Vis (ECCV),2018

3. Multi-exposure image fusion by optimizing a structural similarity index;ma;I IEEE Transactions on Computers,2017

4. BAM: Bottleneck attention module;park;arXiv 1807 06514,2018

5. Deep Prior Guided Network For High-Quality Image Fusion

Cited by 19 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. Leveraging a self-adaptive mean teacher model for semi-supervised multi-exposure image fusion;Information Fusion;2024-12

2. SDFuse: Semantic-injected dual-flow learning for infrared and visible image fusion;Expert Systems with Applications;2024-10

3. Searching a Compact Architecture for Robust Multi-Exposure Image Fusion;IEEE Transactions on Circuits and Systems for Video Technology;2024-07

4. Fusion Network for Infrared and Visible Light Images in Complex Environments;Proceedings of the 2024 3rd International Symposium on Control Engineering and Robotics;2024-05-24

5. Image fusion using Y-net-based extractor and global-local discriminator;Heliyon;2024-05