![]() To the best of our knowledge, the explorations into single image raindrop removal are still limited in two ways. However, these methods are difficult to extend to common situations where only a single image can be available. Benefiting from the temporal correlation between consecutive frames, video-based deraining methods can achieve significant improvements. Recently, raindrop removal has drawn great attention due to its great practicality and challenges. Moreover, the movement of raindrops depends not only on the affinity of the surfaces but also the fusion between different raindrops, which is rather than rain streaks falling along specific directions. Second, due to the different transparency levels, the visibility of regions occluded by raindrops is inhomogeneous, and the image content seen through raindrops may not belong to the areas blocked by the raindrops. First, due to the diversity of contact surfaces, raindrops usually present diverse changes in shape, scale, and direction. Intuitively, raindrops typically show distinct characteristics and complex changes in several aspects, which bring great challenges for removing raindrops while preserving image details. Since the image formation and physical properties of raindrops are very different from those of rain streaks and rain mist, previous methods cannot be applied directly to raindrop removal. Previous studies on rain removal have achieved great progress and have mainly focused on rain streaks and rain mist. Therefore, removing raindrops from rainy images is highly desirable, especially in complicated outdoor scenes. Moreover, the extension of our method towards the rainy image segmentation and detection tasks validates the practicality of the proposed method in outdoor applications.ĭue to the raindrops adhered to a glass window or camera lens, the images captured in rainy weather suffer from poor visibility, which poses significant risks to many outdoor computer vision tasks, such as pedestrian detection, crowd counting, and person re-identification. Extensive experiments on synthetic and real-world datasets demonstrate that the proposed method achieves significant improvements over the recent state-of-the-art raindrop removal methods. 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. ![]() 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. In this paper, we address these raindrop removal problems from two perspectives. 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. 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. ![]() Previous explorations have mainly been limited in two ways. Removing raindrops from a single image is a challenging problem due to the complex changes in shape, scale, and transparency among raindrops. ![]()
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