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Blind Image Separation Method Based on Cascade Generative Adversarial Networks
- Source :
- Applied Sciences, Vol 11, Iss 9416, p 9416 (2021), Applied Sciences, Volume 11, Issue 20
- Publication Year :
- 2021
- Publisher :
- MDPI AG, 2021.
-
Abstract
- To solve the challenge of single-channel blind image separation (BIS) caused by unknown prior knowledge during the separation process, we propose a BIS method based on cascaded generative adversarial networks (GANs). To ensure that the proposed method can perform well in different scenarios and to address the problem of an insufficient number of training samples, a synthetic network is added to the separation network. This method is composed of two GANs: a U-shaped GAN (UGAN), which is used to learn image synthesis, and a pixel-to-attention GAN (PAGAN), which is used to learn image separation. The two networks jointly complete the task of image separation. UGAN uses the unpaired mixed image and the unmixed image to learn the mixing style, thereby generating an image with the “true” mixing characteristics which addresses the problem of an insufficient number of training samples for the PAGAN. A self-attention mechanism is added to the PAGAN to quickly extract important features from the image data. The experimental results show that the proposed method achieves good results on both synthetic image datasets and real remote sensing image datasets. Moreover, it can be used for image separation in different scenarios which lack prior knowledge and training samples.
- Subjects :
- blind image separation
Technology
Computer science
QH301-705.5
QC1-999
Image (mathematics)
Adversarial system
Mixing (mathematics)
Image separation
General Materials Science
Biology (General)
Instrumentation
QD1-999
Fluid Flow and Transfer Processes
business.industry
Process Chemistry and Technology
Physics
General Engineering
Pattern recognition
Engineering (General). Civil engineering (General)
Computer Science Applications
Separation process
Task (computing)
Chemistry
visual attention
Cascade
Artificial intelligence
generative adversarial networks
TA1-2040
business
remote sensing images
Generative grammar
Subjects
Details
- Language :
- English
- ISSN :
- 20763417
- Volume :
- 11
- Issue :
- 9416
- Database :
- OpenAIRE
- Journal :
- Applied Sciences
- Accession number :
- edsair.doi.dedup.....ba5b31a5627802031e3a4fd8fd904504