EfficientDeRain+: Learning Uncertainty-Aware Filtering via RainMix Augmentation for High-Efficiency Deraining

Abstract

Deraining is a significant and fundamental computer vision task, aiming to remove the rain streaks and accumulations in an image or video. Existing deraining methods usually make heuristic assumptions of the rain model, which compels them to employ complex optimization or iterative refinement for high recovery quality. However, this leads to time-consuming methods and affects the effectiveness of addressing rain patterns, deviating from the assumptions. This paper proposes a simple yet efficient deraining method by formulating deraining as a predictive filtering problem without complex rain model assumptions. Specifically, we identify spatially-variant predictive filtering (SPFilt) that adaptively predicts proper kernels via a deep network to filter different individual pixels. Since the filtering can be implemented via well-accelerated convolution, our method can be significantly efficient. We further propose the EfDeRain+ that contains three main contributions to address residual rain traces, multi-scale, and diverse rain patterns without harming efficiency. First, we propose the uncertainty-aware cascaded predictive filtering (UC-PFilt) that can identify the difficulties of reconstructing clean pixels via predicted kernels and remove the residual rain traces effectively. Second, we design the weight-sharing multi-scale dilated filtering (WS-MS-DFilt) to handle multi-scale rain streaks without harming the efficiency. Third, to eliminate the gap across diverse rain patterns, we propose a novel data augmentation method (i.e., RainMix) to train our deep models. By combining all contributions with sophisticated analysis on different variants, our final method outperforms baseline methods on six single-image deraining datasets and one video-deraining dataset in terms of both recovery quality and speed. In particular, EfDeRain+ can derain within about 6.3 ms on a \(481\times 321\) image and is over 74 times faster than the top baseline method with even better recovery quality. We release code in https://github.com/tsingqguo/efficientderainplus.