Shadow Removal
50 papers with code • 3 benchmarks • 6 datasets
Remove shadow from background
Benchmarks
These leaderboards are used to track progress in Shadow Removal
| Trend | Dataset | Best Model | Paper | Code | Compare |
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ISTD
SRD
Kligler
Jung
Doc3DShade
SD7K
Most implemented papers
Mask-ShadowGAN: Learning to Remove Shadows from Unpaired Data
xw-hu/Mask-ShadowGAN
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ICCV 2019
This paper presents a new method for shadow removal using unpaired data, enabling us to avoid tedious annotations and obtain more diverse training samples.
Stacked Conditional Generative Adversarial Networks for Jointly Learning Shadow Detection and Shadow Removal
IsHYuhi/ST-CGAN_Stacked_Conditional_Generative_Adversarial_Networks
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CVPR 2018
Specifically, a shadow image is fed into the first generator which produces a shadow detection mask.
Towards Ghost-free Shadow Removal via Dual Hierarchical Aggregation Network and Shadow Matting GAN
vinthony/ghost-free-shadow-removal
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With the help of novel masks or scenes, we enhance the current datasets using synthesized shadow images.
Shadow Removal via Shadow Image Decomposition
Training our model on this new augmented ISTD dataset further lowers the RMSE on the shadow area to 7. 4.
RIS-GAN: Explore Residual and Illumination with Generative Adversarial Networks for Shadow Removal
To our best knowledge, we are the first one to explore residual and illumination for shadow removal.
Direction-aware Spatial Context Features for Shadow Detection and Removal
xw-hu/DSC
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This paper presents a novel deep neural network design for shadow detection and removal by analyzing the spatial image context in a direction-aware manner.
Water-Filling: An Efficient Algorithm for Digitized Document Shadow Removal
In this paper, we propose a novel algorithm to rectify illumination of the digitized documents by eliminating shading artifacts.
BEDSR-Net: A Deep Shadow Removal Network From a Single Document Image
IsHYuhi/BEDSR-Net_A_Deep_Shadow_Removal_Network_from_a_Single_Document_Image
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CVPR 2020
For taking advantage of specific properties of document images, a background estimation module is designed for extracting the global background color of the document.
Physics-based Shadow Image Decomposition for Shadow Removal
Inspired by physical models of shadow formation, we use a linear illumination transformation to model the shadow effects in the image that allows the shadow image to be expressed as a combination of the shadow-free image, the shadow parameters, and a matte layer.
Auto-Exposure Fusion for Single-Image Shadow Removal
tsingqguo/exposure-fusion-shadow-removal
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CVPR 2021
We conduct extensive experiments on the ISTD, ISTD+, and SRD datasets to validate our method's effectiveness and show better performance in shadow regions and comparable performance in non-shadow regions over the state-of-the-art methods.
