computer-vision-11

Shadow Detection And Removal

3260 papers • 126 benchmarks • 313 datasets

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Most implemented papers

Direction-Aware Spatial Context Features for Shadow Detection and Removal

P. Heng, Chi-Wing Fu, Xiaowei Hu, Lei Zhu, J. Qin•Fri May 11 2018

This paper designs a weighted cross entropy loss to make effective the training for shadow detection and further adopt the network for shadow removal by using a euclidean loss function and formulating a color transfer function to address the color and luminosity inconsistencies in the training pairs.

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SpA-Former: Transformer image shadow detection and removal via spatial attention

Xiao Feng Zhang, Chaonan Gu, Shangdong Zhu•Tue Jun 21 2022

An end-to-end SpA-Former to recover a shadow-free image from a single shaded image, which is a one-stage network capable of directly learning the mapping function between shadows and no shadows, it does not require a separate shadow detection.

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Conditional GANs for Multi-Illuminant Color Constancy: Revolution or yet Another Approach?

Oleksii Sidorov•Wed Nov 14 2018

A novel view on this classical problem via generative end-to-end algorithm based on image conditioned Generative Adversarial Network is proposed and the largest existing shadow removal dataset is rendered and made publicly available.

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Local Water-Filling Algorithm for Shadow Detection and Removal of Document Images

Bingshu Wang, C. L. P. Chen•Mon Nov 30 2020

The proposed local water-filling method to remove shadows by mapping a document image into a structure of topographic surface can remove the shading artifacts and outperform some state-of-the-art methods, especially for the removal of shadow boundaries.

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Learning From Synthetic Shadows for Shadow Detection and Removal

Naoto Inoue, T. Yamasaki•Mon Jan 04 2021

It is demonstrated that shadow removal models trained on SynShadow perform well in removing shadows with diverse shapes and intensities on some challenging benchmarks, and it is shown that merely fine-tuning from a SynShadow-pre-trained model improves existing shadow detection and removal models.

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NTIRE 2023 Image Shadow Removal Challenge Technical Report: Team IIM_TTI

Yuki Kondo, N. Ukita, Riku Miyata, Fuma Yasue, Taito Naruki•Tue Mar 12 2024

Five key improvements are implemented: image alignment, the introduction of a perceptual quality loss function, the semi-automatic annotation for shadow detection, joint learning of shadow detection and removal, and the introduction of new data augmentation technique"CutShadow" for shadow removal.

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