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Infrared and visible image fusion via detail preserving adversarial learning
- Source :
- Information Fusion. 54:85-98
- Publication Year :
- 2020
- Publisher :
- Elsevier BV, 2020.
-
Abstract
- TargefTablets can be detected easily from the background of infrared images due to their significantly discriminative thermal radiations, while visible images contain textural details with high spatial resolution which are beneficial to the enhancement of target recognition. Therefore, fused images with abundant detail information and effective target areas are desirable. In this paper, we propose an end-to-end model for infrared and visible image fusion based on detail preserving adversarial learning. It is able to overcome the limitations of the manual and complicated design of activity-level measurement and fusion rules in traditional fusion methods. Considering the specific information of infrared and visible images, we design two loss functions including the detail loss and target edge-enhancement loss to improve the quality of detail information and sharpen the edge of infrared targets under the framework of generative adversarial network. Our approach enables the fused image to simultaneously retain the thermal radiation with sharpening infrared target boundaries in the infrared image and the abundant textural details in the visible image. Experiments conducted on publicly available datasets demonstrate the superiority of our strategy over the state-of-the-art methods in both objective metrics and visual impressions. In particular, our results look like enhanced infrared images with clearly highlighted and edge-sharpened targets as well as abundant detail information.
- Subjects :
- Image fusion
Infrared
Computer science
business.industry
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
020206 networking & telecommunications
02 engineering and technology
Sharpening
Convolutional neural network
Image (mathematics)
Discriminative model
Hardware and Architecture
Signal Processing
0202 electrical engineering, electronic engineering, information engineering
Fusion rules
020201 artificial intelligence & image processing
Computer vision
Enhanced Data Rates for GSM Evolution
Artificial intelligence
business
Software
Information Systems
Subjects
Details
- ISSN :
- 15662535
- Volume :
- 54
- Database :
- OpenAIRE
- Journal :
- Information Fusion
- Accession number :
- edsair.doi...........73a1db92002f40c625007b82f94ba2dd