Multi-stage Rain Removal Algorithm Based on Multi-scale Frequency Attention
Rain streaks interfere with the photos the outside vision system takes in rainy weather,which caused lowering the im-age quality and affecting the subsequent vision tasks.Therefore,for the following computer vision tasks,it is very crucial to re-move the rain streaks from the photos and get high-quality images.The goal of the multi-stage rain removal method we present in this research is to recover a high quality image by removing rain streaks from a single rain image using multi-scale frequency at-tention.Firstly,a multi-stage rain removal model is designed by integrating the variety of rain streaks,decomposing the rain streaks removal process into multiple sub-processes,and eliminating rain streaks step by step.Second,a long and short-term memory recurrent network is improved to achieve multi-stage rain streaks removal,in which the frequency attention mechanism is introduced to strengthen the attention to rain streaks and a multi-scale feature extraction method is designed to characterize the global information.This addresses the issue of oversmoothing in the current rain streaks removal algorithms.The detail restoration module's final purpose is to fortify background elements.Experiment results show that the proposed algorithm can effectively re-move rain streaks on both the synthetic data set and the real dataset while preserving complete background information,and has a good rain removal effect.
single image rain removalfrequency attentionconvolutional networksrecurrent neural networkdiscrete cosine transform
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