Publication Type
Conference Proceeding Article
Version
acceptedVersion
Publication Date
9-2019
Abstract
Rain removal aims to remove the rain streaks on rain images. Traditional methods based on convolutional neural network (CNN) have achieved impressive results. However, these methods are under-performed when dealing with tilted rain streaks, because CNN is not equivariant to object rotations. To tackle this problem, we propose the Deep Symmetry Enhanced Network (DSEN) that explicitly extracts and learns from rotation-equivariant features from rain images. In addition, we design a self-refining strategy to remove rain streaks in a coarse-to-fine manner. The key idea is to reuse DSEN with an information link which passes the gradient flow to the finer stage. Extensive experimental results on both synthetic and real-world rain images show that our method of self-refined DSEN yields top performance for rain removal.
Keywords
Image Restoration, Rotation Equivariance, CNN
Discipline
Numerical Analysis and Scientific Computing
Research Areas
Data Science and Engineering
Publication
2019 IEEE International Conference on Image Processing (ICIP): Taipei, September 22-25: Proceedings
First Page
2786
Last Page
2790
ISBN
9781538662496
Identifier
10.1109/ICIP.2019.8803265
Publisher
IEEE
City or Country
Piscataway, NJ
Citation
LIU, Hong; YE, Hanrong; LI, Xia; SHI, Wei; LIU, Mengyuan; and SUN, Qianru.
Self-refining deep symmetry enhanced network for rain removal. (2019). 2019 IEEE International Conference on Image Processing (ICIP): Taipei, September 22-25: Proceedings. 2786-2790.
Available at: https://ink.library.smu.edu.sg/sis_research/4449
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1109/ICIP.2019.8803265