  Single image rain streaks removal is extremely important since rainy images
adversely affect many computer vision systems. Deep learning based methods have
found great success in image deraining tasks. In this paper, we propose a novel
residual-guide feature fusion network, called ResGuideNet, for single image
deraining that progressively predicts highquality reconstruction. Specifically,
we propose a cascaded network and adopt residuals generated from shallower
blocks to guide deeper blocks. By using this strategy, we can obtain a coarse
to fine estimation of negative residual as the blocks go deeper. The outputs of
different blocks are merged into the final reconstruction. We adopt recursive
convolution to build each block and apply supervision to all intermediate
results, which enable our model to achieve promising performance on synthetic
and real-world data while using fewer parameters than previous required.
ResGuideNet is detachable to meet different rainy conditions. For images with
light rain streaks and limited computational resource at test time, we can
obtain a decent performance even with several building blocks. Experiments
validate that ResGuideNet can benefit other low- and high-level vision tasks.
