Raindrops adhered to a glass window or camera lens appear in various blurring degrees and resolutions due to the difference in the degrees of raindrops aggregation. The removal of raindrops from a rainy image remains a challenging task because of the density and diversity of raindrops. The abundant location and blur level information are strong prior guide to the task of raindrop removal. However, existing methods use a binary mask to locate and estimate the raindrop with the value 1 (adhesion of raindrops) and 0 (no adhesion), which ignores the diversity of raindrops. Meanwhile, it is noticed that different scale versions of a rainy image have similar raindrop patterns, which makes it possible to employ such complementary information to represent raindrops. In this work, we first propose a soft mask with the value in [−1,1] indicating the blurring level of the raindrops on the background, and explore the positive effect of the blur degree attribute of raindrops on the task of raindrop removal. Secondly, we explore the multi-scale fusion representation for raindrops based on the deep features of the input multi-scale images. The framework is termed uncertainty guided multi-scale attention network (UMAN). Specifically, we construct a multi-scale pyramid structure and introduce an iterative mechanism to extract blur-level information about raindrops to guide the removal of raindrops at different scales. We further introduce the attention mechanism to fuse the input image with the blur-level information, which will highlight raindrop information and reduce the effects of redundant noise. Our proposed method is extensively evaluated on several benchmark datasets and obtains convincing results.

中文翻译：

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不确定性指导的多尺度注意力网络用于从单个图像去除雨滴

由于雨滴聚集程度的差异，附着在玻璃窗或相机镜头上的雨滴会以各种模糊程度和分辨率出现。由于雨滴的密度和多样性，从多雨图像中去除雨滴仍然是一项艰巨的任务。丰富的位置和模糊度信息是去除雨滴任务的有力先导。但是，现有方法使用二进制掩码来定位和估计值为1（雨滴的附着力）和0（无附着力）的雨滴，而忽略了雨滴的多样性。同时，注意到雨量图像的不同比例版本具有相似的雨滴图案，这使得可以利用这种互补信息来表示雨滴。在这项工作中，我们首先提出一个[[-1，1]指出背景上雨滴的模糊程度，并探索雨滴的模糊度属性对去除雨滴的任务的积极影响。其次，基于输入的多尺度图像的深层特征，探索雨滴的多尺度融合表示。该框架称为不确定性指导的多尺度注意力网络（UMAN）。具体来说，我们构建了一个多尺度的金字塔结构，并引入了一种迭代机制来提取有关雨滴的模糊级别信息，以指导不同尺度雨滴的去除。我们进一步引入注意机制，将输入图像与模糊级别信息融合在一起，这将突出显示雨滴信息并减少冗余噪声的影响。