Shadow Removal

27 papers with code • 2 benchmarks • 2 datasets

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Most implemented papers

Mask-ShadowGAN: Learning to Remove Shadows from Unpaired Data

xw-hu/Mask-ShadowGAN 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.

Towards Ghost-free Shadow Removal via Dual Hierarchical Aggregation Network and Shadow Matting GAN

vinthony/ghost-free-shadow-removal 20 Nov 2019

With the help of novel masks or scenes, we enhance the current datasets using synthesized shadow images.

Shadow Removal via Shadow Image Decomposition

lmhieu612/SID ICCV 2019

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

zhling2020/RIS-GAN 20 Nov 2019

To our best knowledge, we are the first one to explore residual and illumination for shadow removal.

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 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

cvlab-stonybrook/SID 23 Dec 2020

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.

Interactive Removal and Ground Truth for Difficult Shadow Scenes

hangong/deshadow 2 Aug 2016

A user-centric method for fast, interactive, robust and high-quality shadow removal is presented.

User-Assisted Shadow Removal

hangong/deshadow-curve_solution Image and Vision Computing 2018

To relight the image, a dense scale field is produced by in-painting the sparse scales.

Direction-aware Spatial Context Features for Shadow Detection and Removal

xw-hu/DSC 12 May 2018

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.