Back to Search
Start Over
PRAGAN: Progressive Recurrent Attention GAN with Pretrained ViT Discriminator for Single-Image Deraining
- Source :
- Sensors, Vol 22, Iss 24, p 9587 (2022)
- Publication Year :
- 2022
- Publisher :
- MDPI AG, 2022.
-
Abstract
- Images captured in bad weather are not conducive to visual tasks. Rain streaks in rainy images will significantly affect the regular operation of imaging equipment; to solve this problem, using multiple neural networks is a trend. The ingenious integration of network structures allows for full use of the powerful representation and fitting abilities of deep learning to complete low-level visual tasks. In this study, we propose a generative adversarial network (GAN) with multiple attention mechanisms for image rain removal tasks. Firstly, to the best of our knowledge, we propose a pretrained vision transformer (ViT) as the discriminator in GAN for single-image rain removal for the first time. Secondly, we propose a neural network training method that can use a small amount of data for training while maintaining promising results and reliable visual quality. A large number of experiments prove the correctness and effectiveness of our method. Our proposed method achieves better results on synthetic and real image datasets than multiple state-of-the-art methods, even when using less training data.
Details
- Language :
- English
- ISSN :
- 14248220
- Volume :
- 22
- Issue :
- 24
- Database :
- Directory of Open Access Journals
- Journal :
- Sensors
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.8fee78325194c2db5e4b194adef3283
- Document Type :
- article
- Full Text :
- https://doi.org/10.3390/s22249587