From darkness to clarity: A comprehensive review of contemporary image shadow removal research (2017-2023)
The removal of shadows from images is a classic problem in computer vision, aiming to restore the lighting in shadowed areas, thereby reducing the information interference and loss caused by the presence of shadows. In recent years, numerous excellent shadow removal algorithms have emerged, particul...
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| Main Authors: | , , |
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| Format: | Article |
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Elsevier
2024
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| Subjects: | |
| Online Access: | http://eprints.um.edu.my/46899/ https://doi.org/10.1016/j.imavis.2024.105100 |
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| Summary: | The removal of shadows from images is a classic problem in computer vision, aiming to restore the lighting in shadowed areas, thereby reducing the information interference and loss caused by the presence of shadows. In recent years, numerous excellent shadow removal algorithms have emerged, particularly with the rapid development of deep learning technology, which has disrupted traditional physics-based approaches and significantly improved the effectiveness of shadow removal. In this paper, we conduct a comprehensive survey of shadow removal methods published from 2017 to the present. We first introduce background knowledge about image shadow removal, providing detailed explanations of both physics-based and learning-based shadow removal methods. We analyze and compare these algorithms from both quantitative and qualitative perspectives, reassessing all models that provided open-source result sets according to uniform criteria. Additionally, we introduce commonly used datasets and evaluation metrics in the field. Finally, we discuss applications of shadow removal in specific scenarios, along with research challenges and opportunities in this domain. |
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