Abstract
Shadow removal from images and videos is an essential task in computer vision that concentrates on detecting the shadow generated by the obstructed light source, and obtains realistic shadow-free results. In this paper, we present a method based on generative adversarial networks (GANs) for shadow removal by supervised learning. Specifically, we train two generators and two discriminators to learn the mapping between shadow and shadow-free image domains. We employ generative adversarial constraints with cycle consistency and content constraints to learn the mapping efficiently. We also propose an adaptive exposure correction module to handle the over-exposure problem in the shadow area of the result. We additionally present a method for improving the quality of benchmark datasets and eventually achieving better shadow removal results. We also show ablation studies to analyze the importance of the ground-truth data with the adaptive exposure correction module in the proposed framework and explore the impact of using different learning strategies in the presented method. We validate the approach on the available large-scale benchmark Image Shadow Triplets dataset (ISTD), and show quantitative and visual improvements in the state-of-the-art results.
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Acknowledgements
This work was supported by the Science and Engineering Research Board (SERB), Department of Science and Technology (DST), New Delhi, India, under Grant No. CRG/2020/001982.
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Mehta, K., Khare, M., Hati, A. (2023). Integration of GAN and Adaptive Exposure Correction for Shadow Removal. In: Gupta, D., Bhurchandi, K., Murala, S., Raman, B., Kumar, S. (eds) Computer Vision and Image Processing. CVIP 2022. Communications in Computer and Information Science, vol 1777. Springer, Cham. https://doi.org/10.1007/978-3-031-31417-9_13
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