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|a 10.1109/TPAMI.2021.3124934
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|a eng
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| 100 |
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|a Le, Hieu
|e verfasserin
|4 aut
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| 245 |
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|a Physics-Based Shadow Image Decomposition for Shadow Removal
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|c 2022
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|a Text
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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|a Date Revised 08.11.2022
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a We propose a novel deep learning method for shadow removal. Inspired by physical models of shadow formation, we use a linear illumination transformation to model the shadow effects in the image that allows the shadow image to be expressed as a combination of the shadow-free image, the shadow parameters, and a matte layer. We use two deep networks, namely SP-Net and M-Net, to predict the shadow parameters and the shadow matte respectively. This system allows us to remove the shadow effects from images. We then employ an inpainting network, I-Net, to further refine the results. We train and test our framework on the most challenging shadow removal dataset (ISTD). Our method improves the state-of-the-art in terms of mean absolute error (MAE) for the shadow area by 20%. Furthermore, this decomposition allows us to formulate a patch-based weakly-supervised shadow removal method. This model can be trained without any shadow- free images (that are cumbersome to acquire) and achieves competitive shadow removal results compared to state-of-the-art methods that are trained with fully paired shadow and shadow-free images. Last, we introduce SBU-Timelapse, a video shadow removal dataset for evaluating shadow removal methods
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|a Journal Article
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|a Samaras, Dimitris
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g 44(2022), 12 vom: 04. Dez., Seite 9088-9101
|w (DE-627)NLM098212257
|x 1939-3539
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|g volume:44
|g year:2022
|g number:12
|g day:04
|g month:12
|g pages:9088-9101
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|u http://dx.doi.org/10.1109/TPAMI.2021.3124934
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|d 44
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