UnShadowNet: Illumination Critic Guided Contrastive Learning For Shadow Removal
Autor: | Dasgupta, Subhrajyoti, Das, Arindam, Yogamani, Senthil, Das, Sudip, Eising, Ciaran, Bursuc, Andrei, Bhattacharya, Ujjwal |
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Rok vydání: | 2022 |
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Druh dokumentu: | Working Paper |
DOI: | 10.1109/ACCESS.2023.3305576 |
Popis: | Shadows are frequently encountered natural phenomena that significantly hinder the performance of computer vision perception systems in practical settings, e.g., autonomous driving. A solution to this would be to eliminate shadow regions from the images before the processing of the perception system. Yet, training such a solution requires pairs of aligned shadowed and non-shadowed images which are difficult to obtain. We introduce a novel weakly supervised shadow removal framework UnShadowNet trained using contrastive learning. It is composed of a DeShadower network responsible for the removal of the extracted shadow under the guidance of an Illumination network which is trained adversarially by the illumination critic and a Refinement network to further remove artefacts. We show that UnShadowNet can be easily extended to a fully-supervised set-up to exploit the ground-truth when available. UnShadowNet outperforms existing state-of-the-art approaches on three publicly available shadow datasets (ISTD, adjusted ISTD, SRD) in both the weakly and fully supervised setups. Comment: Accepted for publication at IEEE Access, vol. 11, pp. 87760-87774, 2023 |
Databáze: | arXiv |
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