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RXDNFuse: A aggregated residual dense network for infrared and visible image fusion
- Source :
- Information Fusion. 69:128-141
- Publication Year :
- 2021
- Publisher :
- Elsevier BV, 2021.
-
Abstract
- This study proposes a novel unsupervised network for IR/VIS fusion task, termed as RXDNFuse, which is based on the aggregated residual dense network. In contrast to conventional fusion networks, RXDNFuse is designed as an end-to-end model that combines the structural advantages of ResNeXt and DenseNet. Hence, it overcomes the limitations of the manual and complicated design of activity-level measurement and fusion rules. Our method establishes the image fusion problem into the structure and intensity proportional maintenance problem of the IR/VIS images. Using comprehensive feature extraction and combination, RXDNFuse automatically estimates the information preservation degrees of corresponding source images, and extracts hierarchical features to achieve effective fusion. Moreover, we design two loss function strategies to optimize the similarity constraint and the network parameter training, thus further improving the quality of detailed information. We also generalize RXDNFuse to fuse images with different resolutions and RGB scale images. Extensive qualitative and quantitative evaluations reveal that our results can effectively preserve the abundant textural details and the highlighted thermal radiation information. In particular, our results form a comprehensive representation of scene information, which is more in line with the human visual perception system.
- Subjects :
- Image fusion
Fusion
Similarity (geometry)
Computer science
business.industry
Feature extraction
020206 networking & telecommunications
Pattern recognition
02 engineering and technology
Residual
Hardware and Architecture
Signal Processing
0202 electrical engineering, electronic engineering, information engineering
Fusion rules
RGB color model
020201 artificial intelligence & image processing
Artificial intelligence
business
Representation (mathematics)
Software
Information Systems
Subjects
Details
- ISSN :
- 15662535
- Volume :
- 69
- Database :
- OpenAIRE
- Journal :
- Information Fusion
- Accession number :
- edsair.doi...........4a933f63f823fd4b58ac103346e46f01
- Full Text :
- https://doi.org/10.1016/j.inffus.2020.11.009