S3R-Net: A Single-Stage Approach to Self-Supervised Shadow Removal
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arXiv
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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866910414565015552 |
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| author | Kubiak, Nikolina Mustafa, Armin Phillipson, Graeme Jolly, Stephen Hadfield, Simon |
| author_facet | Kubiak, Nikolina Mustafa, Armin Phillipson, Graeme Jolly, Stephen Hadfield, Simon |
| contents | In this paper we present S3R-Net, the Self-Supervised Shadow Removal Network. The two-branch WGAN model achieves self-supervision relying on the unify-and-adaptphenomenon - it unifies the style of the output data and infers its characteristics from a database of unaligned shadow-free reference images. This approach stands in contrast to the large body of supervised frameworks. S3R-Net also differentiates itself from the few existing self-supervised models operating in a cycle-consistent manner, as it is a non-cyclic, unidirectional solution. The proposed framework achieves comparable numerical scores to recent selfsupervised shadow removal models while exhibiting superior qualitative performance and keeping the computational cost low. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2404_12103 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | S3R-Net: A Single-Stage Approach to Self-Supervised Shadow Removal Kubiak, Nikolina Mustafa, Armin Phillipson, Graeme Jolly, Stephen Hadfield, Simon Computer Vision and Pattern Recognition Graphics In this paper we present S3R-Net, the Self-Supervised Shadow Removal Network. The two-branch WGAN model achieves self-supervision relying on the unify-and-adaptphenomenon - it unifies the style of the output data and infers its characteristics from a database of unaligned shadow-free reference images. This approach stands in contrast to the large body of supervised frameworks. S3R-Net also differentiates itself from the few existing self-supervised models operating in a cycle-consistent manner, as it is a non-cyclic, unidirectional solution. The proposed framework achieves comparable numerical scores to recent selfsupervised shadow removal models while exhibiting superior qualitative performance and keeping the computational cost low. |
| title | S3R-Net: A Single-Stage Approach to Self-Supervised Shadow Removal |
| topic | Computer Vision and Pattern Recognition Graphics |
| url | https://arxiv.org/abs/2404.12103 |