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Yang K, Xiang W, Chen Z, Liu Y. FERFusion: A Fast and Efficient Recursive Neural Network for Infrared and Visible Image Fusion. SENSORS (BASEL, SWITZERLAND) 2024; 24:2466. [PMID: 38676083 PMCID: PMC11053569 DOI: 10.3390/s24082466] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/05/2024] [Revised: 04/07/2024] [Accepted: 04/08/2024] [Indexed: 04/28/2024]
Abstract
The rapid development of deep neural networks has attracted significant attention in the infrared and visible image fusion field. However, most existing fusion models have many parameters and consume high computational and spatial resources. This paper proposes a fast and efficient recursive fusion neural network model to solve this complex problem that few people have touched. Specifically, we designed an attention module combining a traditional fusion knowledge prior with channel attention to extract modal-specific features efficiently. We used a shared attention layer to perform the early fusion of modal-shared features. Adopting parallel dilated convolution layers further reduces the network's parameter count. Our network is trained recursively, featuring minimal model parameters, and requires only a few training batches to achieve excellent fusion results. This significantly reduces the consumption of time, space, and computational resources during model training. We compared our method with nine SOTA methods on three public datasets, demonstrating our method's efficient training feature and good fusion results.
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Affiliation(s)
- Kaixuan Yang
- Key Laboratory of Opto-Electronic Information Processing, Chinese Academy of Sciences, Shenyang 110016, China; (K.Y.); (W.X.); (Z.C.)
- Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
- Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Wei Xiang
- Key Laboratory of Opto-Electronic Information Processing, Chinese Academy of Sciences, Shenyang 110016, China; (K.Y.); (W.X.); (Z.C.)
- Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
| | - Zhenshuai Chen
- Key Laboratory of Opto-Electronic Information Processing, Chinese Academy of Sciences, Shenyang 110016, China; (K.Y.); (W.X.); (Z.C.)
- Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
- Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yunpeng Liu
- Key Laboratory of Opto-Electronic Information Processing, Chinese Academy of Sciences, Shenyang 110016, China; (K.Y.); (W.X.); (Z.C.)
- Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
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Wu Z, Trigo V. Impact of information system integration on the healthcare management and medical services. INTERNATIONAL JOURNAL OF HEALTHCARE MANAGEMENT 2021. [DOI: 10.1080/20479700.2020.1760015] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Affiliation(s)
- Zijian Wu
- Clinical Research Management Department, Cancer Centre of Guangzhou Medical University, Yuexiu District, People’s Republic of China
| | - Virginia Trigo
- Business School, InstitutoUniversitario de Lisboa (ISCTE–IUL), Lisbon, Portugal
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Sharma G, Prasad C, Srinivasa Rao M. Industrial engineering into healthcare – A comprehensive review. INTERNATIONAL JOURNAL OF HEALTHCARE MANAGEMENT 2020. [DOI: 10.1080/20479700.2020.1757874] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Affiliation(s)
- G.V.S.S. Sharma
- Mechanical Engineering Department, GMR Institute of Technology, Rajam, India
| | - C.L.V.R.S.V. Prasad
- Mechanical Engineering Department, GMR Institute of Technology, Rajam, India
| | - M. Srinivasa Rao
- Mechanical Engineering Department, GMR Institute of Technology, Rajam, India
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