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https://github.com/GuoLanqing/Awesome-Shadow-Removal

Collection of recent shadow removal works, including papers, codes, datasets, and metrics.
https://github.com/GuoLanqing/Awesome-Shadow-Removal
List: Awesome-Shadow-Removal
deep-learning image-enhancement image-restoration shadow-removal summary
Last synced: about 2 months ago
JSON representation
Collection of recent shadow removal works, including papers, codes, datasets, and metrics.
Host: GitHub
URL: https://github.com/GuoLanqing/Awesome-Shadow-Removal
Owner: GuoLanqing
Created: 2021-09-29T03:05:47.000Z (about 3 years ago)
Default Branch: main
Last Pushed: 2024-04-16T06:33:04.000Z (8 months ago)
Last Synced: 2024-05-22T23:00:45.728Z (7 months ago)
Topics: deep-learning, image-enhancement, image-restoration, shadow-removal, summary
Homepage:
Size: 86.9 KB
Stars: 184
Watchers: 15
Forks: 12
Open Issues: 5
Metadata Files:
- Readme: README.md
Awesome Lists containing this project

ultimate-awesome - Awesome-Shadow-Removal - Collection of recent shadow removal works, including papers, codes, datasets, and metrics. (Other Lists / Monkey C Lists)
README

        # Awesome-Shadow-Removal

Collection of recent shadow removal works. Questions and discussions are most welcome! Upcoming works will be updated on a regular basis, feel free to contact me to add... :thumbsup:

## Papers and Codes

### Supervised-Deep-Learning Algorithm 

* `CVPR2017` DeshadowNet: A Multi-Context Embedding Deep Network for Shadow Removal [[Paper]](https://openaccess.thecvf.com/content_cvpr_2017/papers/Qu_DeshadowNet_A_Multi-Context_CVPR_2017_paper.pdf) [[Code]](https://github.com/zylix666/DeshadowNet)

* `CVPR2018` Stacked Conditional Generative Adversarial Networks for Jointly Learning Shadow Detection and Shadow Removal [[Paper]](https://arxiv.org/pdf/1712.02478v1.pdf) [[Code]](https://github.com/IsHYuhi/ST-CGAN_Stacked_Conditional_Generative_Adversarial_Networks)

* `ICCV2019` Shadow Removal via Shadow Image Decomposition [[Paper]](https://openaccess.thecvf.com/content_ICCV_2019/papers/Le_Shadow_Removal_via_Shadow_Image_Decomposition_ICCV_2019_paper.pdf) [[Code]](https://github.com/cvlab-stonybrook/SID)

* `TPAMI2019` Direction-Aware Spatial Context Features for Shadow Detection and Removal [[Paper]](https://arxiv.org/pdf/1805.04635.pdf) [[Code]](https://github.com/xw-hu/DSC)

* `AAAI2020` RIS-GAN: Explore Residual and Illumination with Generative Adversarial Networks for Shadow Removal [[Paper]](http://www.chengjianglong.com/publications/RISGAN_AAAI.pdf) [[Code]](https://github.com/zhling2020/RIS-GAN)

* `AAAI2020` Towards Ghost-Free Shadow Removal via Dual Hierarchical Aggregation Network and Shadow Matting GAN [[Paper]](https://ojs.aaai.org/index.php/AAAI/article/view/6695) [[Code]](https://github.com/vinthony/ghost-free-shadow-removal)

* `SPL2021` Mask-ShadowNet: Towards Shadow Removal via Masked Adaptive Instance Normalization [[Paper]](https://ieeexplore.ieee.org/document/9408351) [[Code]](https://github.com/penguinbing/Mask-ShadowNet)

* `CVPR2021` Auto-Exposure Fusion for Single-Image Shadow Removal [[Paper]](https://openaccess.thecvf.com/content/CVPR2021/html/Fu_Auto-Exposure_Fusion_for_Single-Image_Shadow_Removal_CVPR_2021_paper.html) [[Code]](https://github.com/tsingqguo/exposure-fusion-shadow-removal)

* `ICCV2021` CANet: A Context-Aware Network for Shadow Removal [[Paper]](https://arxiv.org/pdf/2108.09894.pdf) [[Code]](https://github.com/Zipei-Chen/CANet)

* `TPAMI21` Physics-based Shadow Image Decomposition for Shadow Removal [[Paper]](https://arxiv.org/pdf/2012.13018.pdf)

* `AAAI2022` Efficient Model-Driven Network for Shadow Removal [[Paper]](https://ojs.aaai.org/index.php/AAAI/article/view/20276) [[Code]](https://github.com/zhuyr97/AAAI2022_Unfolding_Network_Shadow_Removal)

* `Arxiv2022` UnShadowNet: Illumination Critic Guided Contrastive Learning For Shadow Removal [[Paper]](https://arxiv.org/pdf/2203.15441v1.pdf)

* `CVPR2022` Bijective Mapping Network for Shadow Removal [[Paper]](https://openaccess.thecvf.com/content/CVPR2022/papers/Zhu_Bijective_Mapping_Network_for_Shadow_Removal_CVPR_2022_paper.pdf) [[Code]]( https://github.com/KevinJ-Huang/BMNet)

* `Arxiv2022` CRFormer: A Cross-Region Transformer for Shadow Removal [[Paper]](https://arxiv.org/pdf/2207.01600.pdf)  

* `Arxiv2022` Shadow-Aware Dynamic Convolution for Shadow Removal [[Paper]](https://arxiv.org/pdf/2205.04908.pdf) [[Code]]( https://github.com/xuyimin0926/SADC) 

* `ECCVW2022` CNSNet: A Cleanness-Navigated-Shadow Network for Shadow Removal [[Paper]](https://arxiv.org/pdf/2209.02174.pdf) 

* `ECCV2022` Style-Guided Shadow Removal [[Paper]](https://www.ecva.net/papers/eccv_2022/papers_ECCV/papers/136790353.pdf) [[Code]](https://github.com/jinwan1994/SG-ShadowNet)

* `CVPR2023` ShadowDiffusion: When Degradation Prior Meets Diffusion Model for Shadow Removal [[Paper]](https://arxiv.org/pdf/2212.04711.pdf)

* `AAAI2023` ShadowFormer: Global Context Helps Image Shadow Removal [[Paper]](https://arxiv.org/pdf/2302.01650.pdf) [[Code]](https://github.com/GuoLanqing/ShadowFormer)

* `WACV2023` SHARDS: Efficient SHAdow Removal using Dual Stage Network for

High-Resolution Images [[Paper]](https://openaccess.thecvf.com/content/WACV2023/papers/Sen_SHARDS_Efficient_Shadow_Removal_Using_Dual_Stage_Network_for_High-Resolution_WACV_2023_paper.pdf)

* `WACV2023` LRA&LDRA: Rethinking Residual Predictions for Efficient Shadow Detection

and Removal [[Paper]](https://openaccess.thecvf.com/content/WACV2023/papers/Yucel_LRALDRA_Rethinking_Residual_Predictions_for_Efficient_Shadow_Detection_and_Removal_WACV_2023_paper.pdf)

* `ACMMM2023` FSR-Net: Deep Fourier Network for Shadow Removal [[Paper]](https://dl.acm.org/doi/10.1145/3581783.3612359)

* `TMM2023` A Boundary-Aware Network for Shadow Removal [[Paper]](https://ieeexplore.ieee.org.remotexs.ntu.edu.sg/abstract/document/9918057)

* `TMM2023` A Decoupled Multi-Task Network for Shadow Removal [[Paper]](https://ieeexplore.ieee.org.remotexs.ntu.edu.sg/abstract/document/10058544) [[Code]](https://github.com/nachifur/DMTN)

* `TNNLS2023` A Shadow Imaging Bilinear Model and Three-branch Residual Network for Shadow Removal [[Code]](https://github.com/nachifur/TBRNet)

* `ICCV2023` Leveraging Inpainting for Single-Image Shadow Removal [[Paper]](https://www.google.com.hk/url?sa=t&rct=j&q=&esrc=s&source=web&cd=&ved=2ahUKEwj3_s7gjeyEAxVV7zgGHUU3BewQFnoECBMQAQ&url=https%3A%2F%2Fopenaccess.thecvf.com%2Fcontent%2FICCV2023%2Fpapers%2FLi_Leveraging_Inpainting_for_Single-Image_Shadow_Removal_ICCV_2023_paper.pdf&usg=AOvVaw1FFfPxI0SlYMJIxSRJLfw4&opi=89978449) [[Code]](https://github.com/tsingqguo/inpaint4shadow)

* `AAAI2024` Recasting Regional Lighting for Shadow Removal [[Paper]](https://arxiv.org/pdf/2402.00341.pdf)

* `AAAI2024` DeS3: Adaptive Attention-driven Self and Soft Shadow Removal using ViT Similarity [[Paper]](https://arxiv.org/abs/2211.08089)

* `WACV2024` Latent Feature-Guided Diffusion Models for Shadow Removal [[Paper]](https://openaccess.thecvf.com/content/WACV2024/papers/Mei_Latent_Feature-Guided_Diffusion_Models_for_Shadow_Removal_WACV_2024_paper.pdf) [[Code]](https://github.com/MKFMIKU/Instance-Shadow-Diffusion)

* `CVPR2024` HomoFormer: Homogenized Transformer for Image Shadow Removal [[Paper]](https://openaccess.thecvf.com/content/CVPR2024/papers/Xiao_HomoFormer_Homogenized_Transformer_for_Image_Shadow_Removal_CVPR_2024_paper.pdf)[[Code]](https://github.com/jiexiaou/HomoFormer)

### Unsupervised-Deep-Learning Algorithm

* `ICCV2019` Mask-ShadowGAN: Learning to Remove Shadows from Unpaired Data [[Paper]](https://arxiv.org/abs/1903.10683) [[Code]](https://github.com/xw-hu/Mask-ShadowGAN)

* `ECCV2020` From Shadow Segmentation to Shadow Removal [[Paper]](https://arxiv.org/pdf/2008.00267.pdf) [[Code]](https://github.com/lmhieu612/FSS2SR)

* `CVPR2021` From Shadow Generation to Shadow Removal [[Paper]](https://arxiv.org/pdf/2103.12997v1.pdf) [[Code]](https://github.com/hhqweasd/G2R-ShadowNet)

* `TIP2021` Shadow Removal by a Lightness-Guided Network With Training on Unpaired Data [[Paper]](https://ieeexplore.ieee.org/abstract/document/9318562) [[Code]](https://github.com/hhqweasd/LG-ShadowNet)

* `Arxiv2021` Self-Supervised Shadow Removal [[Paper]](https://arxiv.org/pdf/2010.11619.pdf)

* `Arxiv2021` Unsupervised Shadow Removal Using Target Consistency Generative Adversarial Network [[Paper]](https://arxiv.org/abs/2010.01291)

* `ICCV2021` DC-ShadowNet: Single-Image Hard and Soft Shadow Removal Using Unsupervised Domain-Classifier Guided Network [[Paper]](https://openaccess.thecvf.com/content/ICCV2021/papers/Jin_DC-ShadowNet_Single-Image_Hard_and_Soft_Shadow_Removal_Using_Unsupervised_Domain-Classifier_ICCV_2021_paper.pdf) [[Code]](https://github.com/jinyeying/DC-ShadowNet-Hard-and-Soft-Shadow-Removal)

* `ICCV2023` Boundary-Aware Divide and Conquer: A Diffusion-based Solution for Unsupervised Shadow Removal [[Paper]](http://openaccess.thecvf.com/content/ICCV2023/papers/Guo_Boundary-Aware_Divide_and_Conquer_A_Diffusion-Based_Solution_for_Unsupervised_Shadow_ICCV_2023_paper.pdf)

### Semi-Supervised-Deep-Learning Algorithm

* `ICCV2019` ARGAN: Attentive Recurrent Generative Adversarial Network for Shadow

Detection and Removal [[Paper]](https://openaccess.thecvf.com/content_ICCV_2019/papers/Ding_ARGAN_Attentive_Recurrent_Generative_Adversarial_Network_for_Shadow_Detection_and_ICCV_2019_paper.pdf)

### Portrait Shadow Removal

* `SIGGRAPH2020` Portrait Shadow Manipulation [[Paper]](https://arxiv.org/abs/2005.08925) [[Code]](https://github.com/google/portrait-shadow-manipulation)

* `ACM MM2021` Unsupervised Portrait Shadow Removal via Generative Priors [[Paper]](https://arxiv.org/pdf/2108.03466.pdf) [[Code]](https://github.com/YingqingHe/Shadow-Removal-via-Generative-Priors)

* `BMVC2022` 

Blind Removal of Facial Foreign Shadows  [[Paper]](http://cvlab.cse.msu.edu/pdfs/Liu_Hou_Huang_Ren_Liu_BMVC2022.pdf)

### Shadow Generation

* `AAAI2020` Towards Ghost-Free Shadow Removal via Dual Hierarchical Aggregation Network and Shadow Matting GAN [[Paper]](https://ojs.aaai.org/index.php/AAAI/article/view/6695) [[Code]](https://github.com/vinthony/ghost-free-shadow-removal)

* `TCSVT2021` Learning from Synthetic Shadows for Shadow Detection and Removal [[Paper]](https://arxiv.org/abs/2101.01713) [[Code]](https://github.com/naoto0804/SynShadow)

* `CVPR2021` From Shadow Generation to Shadow Removal [[Paper]](https://arxiv.org/pdf/2103.12997v1.pdf) [[Code]](https://github.com/hhqweasd/G2R-ShadowNet)

* `CVPR2020` Learning to Shadow Hand-Drawn Sketches [[Paper]](https://openaccess.thecvf.com/content_CVPR_2020/papers/Zheng_Learning_to_Shadow_Hand-Drawn_Sketches_CVPR_2020_paper.pdf) [[Code]](https://cal.cs.umbc.edu/Papers/Zheng-2020-Shade/index.html)

* `ICCV2021` SmartShadow: Artistic Shadow Drawing Tool for Line Drawings [[Paper]](https://openaccess.thecvf.com/content/ICCV2021/papers/Zhang_SmartShadow_Artistic_Shadow_Drawing_Tool_for_Line_Drawings_ICCV_2021_paper.pdf)

* `CVPR2024` Shadow Generation for Composite Image Using Diffusion Model [[Paper]](https://arxiv.org/abs/2104.10338) [[Code]](https://github.com/bcmi/Object-Shadow-Generation-Dataset-DESOBAv2)

### Application

* `ICASSP2020` Shadow removal of text document images by estimating local and global background colors [[Paper]](https://ieeexplore.ieee.org/document/9053378)

* `CVPR2020` BEDSR-Net: A Deep Shadow Removal Network From a Single Document Image [[Paper]](https://openaccess.thecvf.com/content_CVPR_2020/html/Lin_BEDSR-Net_A_Deep_Shadow_Removal_Network_From_a_Single_Document_CVPR_2020_paper.html)

* `CVPR2022` Shadows Can Be Dangerous: Stealthy and Effective Physical-World Adversarial Attack by Natural Phenomenon [[Paper]](https://arxiv.org/pdf/2203.03818.pdf) [[Code]](https://github.com/hncszyq/ShadowAttack)

* `TGRS2022` Shadow Removal of Hyperspectral Remote Sensing Images With Multiexposure Fusion [[Paper]](https://ieeexplore.ieee.org/abstract/document/9874905)

* `AAAI2023` Estimating Reflectance Layer from A Single Image: Integrating Reflectance Guidance and Shadow/Specular Aware Learning [[Paper]](https://arxiv.org/pdf/2211.14751.pdf)

## Datasets

* ISTD [[link]](https://github.com/DeepInsight-PCALab/ST-CGAN)  

* ISTD+ [[link]](https://github.com/cvlab-stonybrook/SID)

* SRD [[Training]](https://drive.google.com/file/d/1W8vBRJYDG9imMgr9I2XaA13tlFIEHOjS/view)[[Testing]](https://drive.google.com/file/d/1GTi4BmQ0SJ7diDMmf-b7x2VismmXtfTo/view)

* USR: Unpaired Shadow Removal Dataset [[link]](https://drive.google.com/file/d/1PPAX0W4eyfn1cUrb2aBefnbrmhB1htoJ/view)

## Shadow Detectors

* `CVPR2018` Direction-Aware Spatial Context Features for Shadow Detection [[Paper]](https://openaccess.thecvf.com/content_cvpr_2018/papers/Hu_Direction-Aware_Spatial_Context_CVPR_2018_paper.pdf) [[Code]](https://github.com/xw-hu/DSC)

* `ECCV2018` Bidirectional Feature Pyramid Network with Recurrent Attention Residual Modules for Shadow Detection [[Paper]](https://openaccess.thecvf.com/content_ECCV_2018/papers/Lei_Zhu_Bi-directional_Feature_Pyramid_ECCV_2018_paper.pdf) [[Code]](https://github.com/zijundeng/BDRAR)

* `AAAI2020` Towards Ghost-Free Shadow Removal via Dual Hierarchical Aggregation Network and Shadow Matting GAN [[Paper]](https://ojs.aaai.org/index.php/AAAI/article/view/6695) [[Code]](https://github.com/vinthony/ghost-free-shadow-removal)

* `ACM MM2022` Single Image Shadow Detection via Complementary Mechanism [[Paper]](https://xueyangfu.github.io/paper/2022/ACMMM/Shadow-Detection.pdf) [[Code]](https://github.com/zhuyr97/SDCM)

* `Arxiv2023` SAM Fails to Segment Anything? -- SAM-Adapter: Adapting SAM in Underperformed Scenes: Camouflage, Shadow, Medical Image Segmentation, and More [[Paper]](https://arxiv.org/abs/2304.09148) [[Code]](https://github.com/tianrun-chen/SAM-Adapter-PyTorch)

## Instance Shadow Detectors

* `CVPR2020` Instance Shadow Detection [[Paper]](https://openaccess.thecvf.com/content_CVPR_2020/papers/Wang_Instance_Shadow_Detection_CVPR_2020_paper.pdf) [[Code]](https://github.com/stevewongv/InstanceShadowDetection)

* `CVPR2021` Single-Stage Instance Shadow Detection with Bidirectional Relation Learning [[Paper]](https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_Single-Stage_Instance_Shadow_Detection_With_Bidirectional_Relation_Learning_CVPR_2021_paper.pdf) [[Code]](https://github.com/stevewongv/SSIS)

* `TPAMI2023` Instance Shadow Detection with A Single-Stage Detector [[Paper]](https://arxiv.org/abs/2207.04614) [[Code]](https://github.com/stevewongv/SSIS)

* `Arxiv2023` Video Instance Shadow Detection [[Paper]](https://arxiv.org/abs/2211.12827)

## Metrics

 * RMSE (Root-Mean-Square Error) [[Wiki]](https://en.wikipedia.org/wiki/Root-mean-square_deviation) [[Matlab Code]](https://www.mathworks.com/matlabcentral/fileexchange/21383-rmse) 

 * PSNR (Peak Signal-to-Noise Ratio) [[Wiki]](https://en.wikipedia.org/wiki/Peak_signal-to-noise_ratio) [[Matlab Code]](https://www.mathworks.com/help/images/ref/psnr.html) [[Python Code]](https://github.com/aizvorski/video-quality)

 * SSIM (Structural similarity) [[Wiki]](https://en.wikipedia.org/wiki/Structural_similarity) [[Matlab Code]](http://www.cns.nyu.edu/~lcv/ssim/ssim_index.m) [[Python Code]](https://github.com/aizvorski/video-quality/blob/master/ssim.py)

 * NIQE (Naturalness Image Quality Evaluator) [[Web]](http://live.ece.utexas.edu/research/Quality/nrqa.htm) [[Matlab Code]](http://live.ece.utexas.edu/research/Quality/nrqa.htm) [[Python Code]](https://github.com/aizvorski/video-quality/blob/master/niqe.py)

## Citation

If you find this work useful in your research, Please cite the paper as below:

```bib

@article{guo2024single,

  title={Single-Image Shadow Removal Using Deep Learning: A Comprehensive Survey},

  author={Guo, Laniqng and Wang, Chong and Wang, Yufei and Huang, Siyu and Yang, Wenhan and Kot, Alex C and Wen, Bihan},

  journal={arXiv preprint arXiv:2407.08865},

  year={2024}

}

```