# Uncertainty Guided Multi-Scale Residual Learning-using a Cycle Spinning CNN for Single Image De-Raining

## TL;DR

This paper introduces a novel uncertainty-guided multi-scale residual CNN with cycle spinning for single image de-raining, effectively handling rain streaks of varying size, direction, and density, and significantly improving de-raining performance.

## Contribution

It proposes a new multi-scale residual learning framework with uncertainty guidance and cycle spinning, addressing limitations of prior methods by incorporating rain location information.

## Key findings

- Achieves significant improvements over state-of-the-art methods on synthetic datasets.
- Demonstrates robustness on real rainy images.
- Provides publicly available code for reproducibility.

## Abstract

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