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.

Deraining 是一项重要且基本的计算机视觉任务，旨在消除图像或视频中的雨条纹和堆积物。现有的 deraining 方法通常对 rain 模型进行启发式假设，这迫使它们采用复杂的优化或迭代优化来实现高恢复质量。然而，这会导致耗时的方法，并影响解决降雨模式的有效性，偏离假设。本文提出了一种简单而有效的 deraining 方法，将 deraining 表述为没有复杂降雨模型假设的预测过滤问题。具体来说，我们确定了空间变体预测过滤 （SPFilt），它通过深度网络自适应地预测适当的内核，以过滤不同的单个像素。由于过滤可以通过加速良好的卷积来实现，因此我们的方法可以非常高效。我们进一步提出了 EfDeRain+，它包含三个主要贡献，以解决残余雨迹、多尺度和多样化的降雨模式，而不会损害效率。首先，我们提出了不确定性感知级联预测过滤 （UC-PFilt），它可以识别通过预测核重建干净像素的困难，并有效去除残余雨迹。其次，我们设计了权重共享多尺度扩张滤波 （WS-MS-DFilt） 来处理多尺度的连续降雨，而不会损害效率。第三，为了消除不同降雨模式之间的差距，我们提出了一种新的数据增强方法（即 RainMix）来训练我们的深度模型。 通过将所有贡献与对不同变体的复杂分析相结合，我们的最终方法在恢复质量和速度方面在六个单图像 deraining 数据集和一个视频 deraining 数据集上优于基线方法。特别是，EfDeRain+ 可以在大约 6.3 毫秒内对 \（481\times 321\） 图像进行修复，并且比最高基线方法快 74 倍以上，恢复质量更好。我们以 https://github.com/tsingqguo/efficientderainplus 发布代码。