Lightweight Pyramid Networks for Image Deraining

Existing deep convolutional neural networks (CNNs) have found major success in image deraining, but at the expense of an enormous number of parameters. This limits their potential applications, e.g., in mobile devices. In this paper, we propose a lightweight pyramid networt (LPNet) for single-image...

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Bibliographic Details
Published in:IEEE transaction on neural networks and learning systems 2020-06, Vol.31 (6), p.1794-1807
Main Authors: Fu, Xueyang, Liang, Borong, Huang, Yue, Ding, Xinghao, Paisley, John
Format: Article
Language:English
Subjects:
Artificial neural networks
Computer vision
Deep convolutional neural network (CNN)
Electronic devices
Feature extraction
image pyramid
Knowledge engineering
Laplace equations
Learning
Learning systems
Lightweight
lightweight networks
Neural networks
Parameters
Rain
rain removal
residual learning
Task analysis
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Summary:Existing deep convolutional neural networks (CNNs) have found major success in image deraining, but at the expense of an enormous number of parameters. This limits their potential applications, e.g., in mobile devices. In this paper, we propose a lightweight pyramid networt (LPNet) for single-image deraining. Instead of designing a complex network structure, we use domain-specific knowledge to simplify the learning process. In particular, we find that by introducing the mature Gaussian-Laplacian image pyramid decomposition technology to the neural network, the learning problem at each pyramid level is greatly simplified and can be handled by a relatively shallow network with few parameters. We adopt recursive and residual network structures to build the proposed LPNet, which has less than 8K parameters while still achieving the state-of-the-art performance on rain removal. We also discuss the potential value of LPNet for other low- and high-level vision tasks.
ISSN:2162-237X
2162-2388
DOI:10.1109/TNNLS.2019.2926481