SAM-helps-Shadow: When Segment Anything Model meet shadow removal
TL
TL;DR
This study innovatively adapted the SAM (Segment anything model) for shadow removal by introducing SAM-helps-Shadow, effectively integrating shadow detection and removal into a single stage.
Abstract
The challenges surrounding the application of image shadow removal to real-world images and not just constrained datasets like ISTD/SRD have highlighted an urgent need for zero-shot learning in this field. In this study, we innovatively adapted the SAM (Segment anything model) for shadow removal by introducing SAM-helps-Shadow, effectively integrating shadow detection and removal into a single stage. Our approach utilized the model's detection results as a potent prior for facilitating shadow detection, followed by shadow removal using a second-order deep unfolding network. The source code of SAM-helps-Shadow can be obtained from https://github.com/zhangbaijin/SAM-helps-Shadow.
References48 items
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Scaling-up Remote Sensing Segmentation Dataset with Segment Anything Model
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Customized Segment Anything Model for Medical Image Segmentation
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Segment Anything in Non-Euclidean Domains: Challenges and Opportunities
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Inpaint Anything: Segment Anything Meets Image Inpainting
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