A plug and play fuzzy mask extraction module for single image deraining

In this paper, a plug and play fuzzy mask extraction module for single image rain streak removal is proposed. Specifically, fuzzy mask maps of the rain data-set are obtained by optimizing the convex combination of several grouping functions; Based on these fuzzy mask maps as ground truth, we develop a deep learning architecture that learns the fuzzy rain map; We then fix the model to obtain a unified network model as a plug and play fuzzy mask extraction module for Single Image Deraining; When we embed a plug and play fuzzy mask extraction module into a deraining deep neural network architecture, it will improve performance due to fusion with the fine guided information of the fuzzy rain mask map. Our method differs from other mask maps as the fuzzy mask ground truth is extracted based on the pixel-level membership of the background and foreground of the image, so the grey similarity and spatial similarity between each pixel and its neighboring pixels of a single rain image can be expressed more elaborately. We provide a unified fuzzy mask module in image rain removal, which can lessen the burden of designing an attention module. The advantage of our proposed method is, as long as our fuzzy mask extraction module is embedded in any encoding and decoding rain removal network, it can obtain additional guided information such as rainy/non-rainy regions and the degree of degraded image, which can be greatly beneficial in rain detection and removal. Comprehensive experiments show that combining the rain removal network with our proposed model not only improves the rain removal effect of the algorithm, but also gives clearer background details of the image. The proposed fuzzy mask learning model is critically beneficial for either rain removal algorithms.