Paper Info
Title
Progressive Image Deraining Networks: A Better And Simpler Baseline
Abstract
Along with the deraining performance improvement of deep networks, their structures and learning become more and more complicated and diverse, making it difficult to analyze the contribution of various network modules when developing new deraining networks. To handle this issue, this paper provides a better and simpler baseline deraining network by considering network architecture, input and output, and loss functions. Specifically, by repeatedly unfolding a shallow ResNet, progressive ResNet (PRN) is proposed to take advantage of recursive computation. A recurrent layer is further introduced to exploit the dependencies of deep features across stages, forming our progressive recurrent network (PReNet). Furthermore, intra-stage recursive computation of ResNet can be adopted in PRN and PReNet to notably reduce network parameters with unsubstantial degradation in deraining performance. For network input and output, we take both stage-wise result and original rainy image as input to each ResNet and finally output the prediction of residual image. As for loss functions, single MSE or negative SS!M losses are sufficient to train PRN and PReNet. Experiments show that PRN and PReNet perform favorably on both synthetic and real rainy images. Considering its simplicity, efficiency and effectiveness, our models are expected to serve as a suitable baseline in future deraining research. The source codes are available at https://github.com/csdwren/PReNet.
Year
DOI
Venue
2019
10.1109/CVPR.2019.00406
2019 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2019)
Field
DocType
Volume
Computer vision,Computer science,Artificial intelligence
Journal
abs/1901.09221
ISSN
Citations 
PageRank 
1063-6919
21
0.56
References 
Authors
0
5
Name
Order
Citations
PageRank
Dongwei Ren110312.26
Wangmeng Zuo23833173.11
Qinghua Hu34028171.50
Pengfei Zhu479132.66
Deyu Meng52025105.31