  Single image de-raining is an extremely challenging problem since the rainy
image may contain rain streaks which may vary in size, direction and density.
Previous approaches have attempted to address this problem by leveraging some
prior information to remove rain streaks from a single image. One of the major
limitations of these approaches is that they do not consider the location
information of rain drops in the image. The proposed Uncertainty guided
Multi-scale Residual Learning (UMRL) network attempts to address this issue by
learning the rain content at different scales and using them to estimate the
final de-rained output. In addition, we introduce a technique which guides the
network to learn the network weights based on the confidence measure about the
estimate. Furthermore, we introduce a new training and testing procedure based
on the notion of cycle spinning to improve the final de-raining performance.
Extensive experiments on synthetic and real datasets to demonstrate that the
proposed method achieves significant improvements over the recent
state-of-the-art methods. Code is available at:
https://github.com/rajeevyasarla/UMRL--using-Cycle-Spinning
