Sensors, Vol. 20, Pages 6733: Multi-Scale Shape Adaptive Network for Raindrop Detection and Removal from a Single Image

Sensors, Vol. 20, Pages 6733: Multi-Scale Shape Adaptive Network for Raindrop Detection and Removal from a Single Image Sensors doi: 10.3390/s20236733 Authors: Hao Luo Qingbo Wu King Ngi Ngan Hanxiao Luo Haoran Wei Hongliang Li Fanman Meng Linfeng Xu Removing raindrops from a single image is a challenging problem due to the complex changes in shape, scale, and transparency among raindrops. Previous explorations have mainly been limited in two ways. First, publicly available raindrop image datasets have limited capacity in terms of modeling raindrop characteristics (e.g., raindrop collision and fusion) in real-world scenes. Second, recent deraining methods tend to apply shape-invariant filters to cope with diverse rainy images and fail to remove raindrops that are especially varied in shape and scale. In this paper, we address these raindrop removal problems from two perspectives. First, we establish a large-scale dataset named RaindropCityscapes, which includes 11,583 pairs of raindrop and raindrop-free images, covering a wide variety of raindrops and background scenarios. Second, a two-branch Multi-scale Shape Adaptive Network (MSANet) is proposed to detect and remove diverse raindrops, effectively filtering the occluded raindrop regions and keeping the clean background well-preserved. Extensive experiments on synthetic and real-world datasets demonstrate that the proposed method achieves significant improvements over the recent state-of-the-art raindro...
Source: Sensors - Category: Biotechnology Authors: Tags: Letter Source Type: research
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