Back to Search
Start Over
See clearly on rainy days: Hybrid multiscale loss guided multi-feature fusion network for single image rain removal
- Source :
- Computational Visual Media. 7:467-482
- Publication Year :
- 2021
- Publisher :
- Springer Science and Business Media LLC, 2021.
-
Abstract
- The quality of photos is highly susceptible to severe weather such as heavy rain; it can also degrade the performance of various visual tasks like object detection. Rain removal is a challenging problem because rain streaks have different appearances even in one image. Regions where rain accumulates appear foggy or misty, while rain streaks can be clearly seen in areas where rain is less heavy. We propose removing various rain effects in pictures using a hybrid multiscale loss guided multiple feature fusion de-raining network (MSGMFFNet). Specially, to deal with rain streaks, our method generates a rain streak attention map, while preprocessing uses gamma correction and contrast enhancement to enhanced images to address the problem of rain accumulation. Using these tools, the model can restore a result with abundant details. Furthermore, a hybrid multiscale loss combining L1 loss and edge loss is used to guide the training process to pay attention to edge and content information. Comprehensive experiments conducted on both synthetic and real-world datasets demonstrate the effectiveness of our method.
- Subjects :
- business.industry
Computer science
Rain removal
Process (computing)
Training (meteorology)
Streak
02 engineering and technology
Computer Graphics and Computer-Aided Design
Object detection
020303 mechanical engineering & transports
0203 mechanical engineering
Artificial Intelligence
Gamma correction
0202 electrical engineering, electronic engineering, information engineering
Preprocessor
020201 artificial intelligence & image processing
Computer vision
Computer Vision and Pattern Recognition
Artificial intelligence
Enhanced Data Rates for GSM Evolution
business
Subjects
Details
- ISSN :
- 20960662 and 20960433
- Volume :
- 7
- Database :
- OpenAIRE
- Journal :
- Computational Visual Media
- Accession number :
- edsair.doi...........ecd297579d970b56e126d4d9ac11175d