  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 graceful 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 SSIM 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.
