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Shadow removal is still a challenging task due to its inherent background-dependent and spatial-variant properties, leading to unknown and diverse shadow patterns.
Influential observations, high leverage points, and outliers in linear regression
Samprit Chatterjee, Ali S Hadi, et al · 1986
Earlier work this paper cites.
Detecting moving objects, ghosts, and shadows in video streams
Rita Cucchiara, Costantino Grana, Massimo Piccardi, and Andrea Prati · 2003
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Poisson image editing
Patrick Pérez, Michel Gangnet, and Andrew Blake · 2003
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Physical models for moving shadow and object detection in video
Sohail Nadimi and Bir Bhanu · 2004
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Fusion of multi-exposure images
A Ardeshir Goshtasby · 2005
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The shadow meets the mask: Pyramid-based shadow removal
Yael Shor and Dani Lischinski · 2008
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Efficient background subtraction and shadow removal for monochromatic video sequences
Cláudio Rosito Jung · 2009
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Exposure fusion: A simple and practical alternative to high dynamic range photography
Tom Mertens, Jan Kautz, and Frank Van Reeth · 2009
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Improved shadow removal for robust person tracking in surveillance scenarios
Andres Sanin, Conrad Sanderson, and Brian C Lovell · 2010
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Paired regions for shadow detection and removal
Ruiqi Guo, Qieyun Dai, and Derek Hoiem · 2012
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Image fusion with guided filtering
Shutao Li, Xudong Kang, and Jianwen Hu · 2013
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Fast shadow removal using adaptive multi-scale illumination transfer
Chunxia Xiao, Ruiyun She, Donglin Xiao, and Kwan-Liu Ma · 2013
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Learning to remove soft shadows
Maciej Gryka, Michael Terry, and Gabriel J Brostow · 2015
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U-net: Convolutional networks for biomedical image segmentation
Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 2015
Cited alongside, same era.
Shadow remover: Image shadow removal based on illumination recovering optimization
Ling Zhang, Qing Zhang, and Chunxia Xiao · 2015
Cited alongside, same era.
Interactive removal and ground truth for difficult shadow scenes
Han Gong and Darren Cosker · 2016
Cited alongside, same era.
Multi-exposure image fusion by optimizing a structural similarity index
Kede Ma, Zhengfang Duanmu, Hojatollah Yeganeh, and Zhou Wang · 2017
Cited alongside, same era.
Deepfuse: A deep unsupervised approach for exposure fusion with extreme exposure image pairs
K Ram Prabhakar, V Sai Srikar, and R Venkatesh Babu · 2017
Cited alongside, same era.
Deshadownet: A multi-context embedding deep network for shadow removal
Mask-shadowgan: Learning to remove shadows from unpaired data
Xiaowei Hu, Yitong Jiang, Chi-Wing Fu, and Pheng-Ann Heng · 2019
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Shadow removal via shadow image decomposition
Hieu Le and Dimitris Samaras · 2019
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Asymmetric gan for unpaired image-to-image translation
Yu Li, Sheng Tang, Rui Zhang, Yongdong Zhang, Jintao Li, and Shuicheng Yan · 2019
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Deep guided learning for fast multi-exposure image fusion
Kede Ma, Zhengfang Duanmu, Hanwei Zhu, Yuming Fang, and Zhou Wang · 2019
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Adversarial exposure attack on diabetic retinopathy imagery
Y Cheng, F Juefei-Xu, Q Guo, H Fu, X Xie, SW Lin, W Lin, and Y Liu · 2020
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Towards ghost-free shadow removal via dual hierarchical aggregation network and shadow matting gan
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Liangqiong Qu, Jiandong Tian, Shengfeng He, Yandong Tang, and Rynson WH Lau · 2017
Cited alongside, same era.
Leave-one-out kernel optimization for shadow detection and removal
Tomas F Yago Vicente, Minh Hoai, and Dimitris Samaras · 2017
Cited alongside, same era.
Aggregated residual transformations for deep neural networks
Saining Xie, Ross Girshick, Piotr Dollár, Zhuowen Tu, and Kaiming He · 2017
Cited alongside, same era.
Stacked conditional generative adversarial networks for jointly learning shadow detection and shadow removal
Jifeng Wang, Xiang Li, and Jian Yang · 2018
Cited alongside, same era.
Improving shadow suppression for illumination robust face recognition
Wuming Zhang, Xi Zhao, Jean-Marie Morvan, and Liming Chen · 2018
Cited alongside, same era.
Bidirectional feature pyramid network with recurrent attention residual modules for shadow detection
Lei Zhu, Zijun Deng, Xiaowei Hu, Chi-Wing Fu, Xuemiao Xu, Jing Qin, and Pheng-Ann Heng · 2018
Cited alongside, same era.
Argan: Attentive recurrent generative adversarial network for shadow detection and removal
Bin Ding, Chengjiang Long, Ling Zhang, and Chunxia Xiao · 2019
Cited alongside, same era.
Xiaodong Cun, Chi-Man Pun, and Cheng Shi · 2020
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Making images undiscoverable from co-saliency detection
R Gao, Q Guo, F Juefei-Xu, H Yu, X Ren, W Feng, and S Wang · 2020
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Watch out! motion is blurring the vision of your deep neural networks
Qing Guo, Felix Juefei-Xu, Xiaofei Xie, Lei Ma, Jian Wang, Bing Yu, Wei Feng, and Yang Liu · 2020
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SPARK: Spatial-aware Online Incremental Attack Against Visual Tracking
Qing Guo, Xiaofei Xie, Felix Juefei-Xu, Lei Ma, Zhongguo Li, Wanli Xue, Wei Feng, and Yang Liu · 2020
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Direction-aware spatial context features for shadow detection and removal
Xiaowei Hu, Chi-Wing Fu, Lei Zhu, Jing Qin, and Pheng-Ann Heng · 2020
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From shadow segmentation to shadow removal
Hieu Le and Dimitris Samaras · 2020
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Efficientderain: Learning pixel-wise dilation filtering for high-efficiency single-image deraining
Qing Guo, Jingyang Sun, Felix Juefei-Xu, Lei Ma, Xiaofei Xie, Wei Feng, and Yang Liu · 2021
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