Self-refining deep symmetry enhanced network for rain removal

Self-refining deep symmetry enhanced network for rain removal

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 tack...

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Bibliographic Details
Main Authors: LIU, Hong, YE, Hanrong, LI, Xia, SHI, Wei, LIU, Mengyuan, SUN, Qianru
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2019
Subjects:
Image Restoration
Rotation Equivariance
CNN
Numerical Analysis and Scientific Computing
Online Access:https://ink.library.smu.edu.sg/sis_research/4449
https://ink.library.smu.edu.sg/context/sis_research/article/5452/viewcontent/ICIP2019_pre.pdf
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Institution: Singapore Management University
Language: English
Description
Summary: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.

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