IEEE 2017 Conference on
Computer Vision and Pattern
Recognition
Introduction
Motivation
Recurrent Joint Rain Detection and Removal Experiment Results
Deep Joint Rain Detection and Removal from a Single ImageWenhan Yang1, Robby T. Tan2,3, Jiashi Feng2, Jiaying Liu1, Zongming Guo1, and Shuicheng Yan4,2
1Institute of Computer Science and Technology, Peking University, Beijing, China2National University of Singapore, 3Yale-NUS College, 4360 AI Institute
IEEE 2017 Conference on Computer
Vision and Pattern Recognition
Contextualized Dilated Network
Visual comparison
Hard case
- The intrinsic overlapping between
rain streaks and texture.
- Complex degradations, e.g. heavy
rain and mist.
- A limited receptive field.
Challenges
- Region-dependent rain models
Rain-streak binary mask
Diversified streaks & accumulation
- Joint rain detection and removal
- Contextualized dilated network
Contributions
Rain Image Formation
1
1s
t
t
O = B + S R A
Region dependent
- Rain localization.
- Allowing a new rain removal pipeline to detect rain regions first,
and then to operate differently.
Mist and heavy rains
Motivation
- A larger receptive field.
- More context information.
ResNet
- Increasingly larger receptive
fields.
Multi-Path Dilation
Convolutions
- Filter at different scales
- P1: 5×5
- P2: 9×9
- P3: 13×13
Rain detection, estimation and removal
F R, F,R S, , ,
F,R S O RS B
Rain Images DSC[1] LP[2] Ours
For more details & codes, scan QR code or navigate
to http://www.icst.pku.edu.cn/struct/Projects/joint_rain_removal.html
Interested in our team STRUCT? Navigate to
1. Y. Li, et al. Rain streak removal using layer
priors. CVPR, 2016.
2. Y. Luo, et al. Removing rain from a single
image via discriminative sparse coding.
ICCV, 2015.
STRUCT @ PKUSpatial and Temporal Restoration, Understanding and Compression
YaleNUSCollege
NUSNational Universityof Singapore
http://www.icst.pku.edu.cn/struct/struct.html
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