# Shadow Removal by a Lightness-Guided Network with Training on Unpaired Data

arXiv: https://arxiv.org/abs/2006.15617 · HTML: https://mlsanity.com/paper/2006.15617/

## Key facts

- Cited by 38 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jun 2020.

## Built on by (most cited first, 30 of 38)

- [Residual Denoising Diffusion Models](https://mlsanity.com/paper/2308.13712/) (2023)
- [From Shadow Generation to Shadow Removal](https://mlsanity.com/paper/2103.12997/) (2021)
- [CANet: A Context-Aware Network for Shadow Removal](https://mlsanity.com/paper/2108.09894/) (2021)
- [DeS3: Adaptive Attention-driven Self and Soft Shadow Removal using ViT Similarity](https://mlsanity.com/paper/2211.08089/) (2022)
- [High-Resolution Document Shadow Removal via A Large-Scale Real-World Dataset and A Frequency-Aware Shadow Erasing Net](https://mlsanity.com/paper/2308.14221/) (2023)
- [Leveraging Inpainting for Single-Image Shadow Removal](https://mlsanity.com/paper/2302.05361/) (2023)
- [Recasting Regional Lighting for Shadow Removal](https://mlsanity.com/paper/2402.00341/) (2024)
- [Instance Shadow Detection with A Single-Stage Detector](https://mlsanity.com/paper/2207.04614/) (2022)
- [DocDeshadower: Frequency-Aware Transformer for Document Shadow Removal](https://mlsanity.com/paper/2307.15318/) (2023)
- [CRFormer: A Cross-Region Transformer for Shadow Removal](https://mlsanity.com/paper/2207.01600/) (2022)
- [Memory augment is All You Need for image restoration](https://mlsanity.com/paper/2309.01377/) (2023)
- [SpA-Former: Transformer image shadow detection and removal via spatial attention](https://mlsanity.com/paper/2206.10910/) (2022)
- [Single-Image Shadow Removal Using Deep Learning: A Comprehensive Survey](https://mlsanity.com/paper/2407.08865/) (2024)
- [Unveiling Deep Shadows: A Survey and Benchmark on Image and Video Shadow Detection, Removal, and Generation in the Deep Learning Era](https://mlsanity.com/paper/2409.02108/) (2024)
- [SAM-helps-Shadow:When Segment Anything Model meet shadow removal](https://mlsanity.com/paper/2306.06113/) (2023)
- [S3R-Net: A Single-Stage Approach to Self-Supervised Shadow Removal](https://mlsanity.com/paper/2404.12103/) (2024)
- [Structure-Informed Shadow Removal Networks](https://mlsanity.com/paper/2301.03182/) (2023)
- [Learning Physical-Spatio-Temporal Features for Video Shadow Removal](https://mlsanity.com/paper/2303.09370/) (2023)
- [Semantic-guided Adversarial Diffusion Model for Self-supervised Shadow Removal](https://mlsanity.com/paper/2407.01104/) (2024)
- [Learning Restoration is Not Enough: Transfering Identical Mapping for Single-Image Shadow Removal](https://mlsanity.com/paper/2305.10640/) (2023)
- [ShaDocFormer: A Shadow-Attentive Threshold Detector With Cascaded Fusion Refiner for Document Shadow Removal](https://mlsanity.com/paper/2309.06670/) (2023)
- [Deshadow-Anything: When Segment Anything Model Meets Zero-shot shadow removal](https://mlsanity.com/paper/2309.11715/) (2023)
- [Cross-Modal Spherical Aggregation for Weakly Supervised Remote Sensing Shadow Removal](https://mlsanity.com/paper/2406.17469/) (2024)
- [DINOLight: Robust Ambient Light Normalization with Self-supervised Visual Prior Integration](https://mlsanity.com/paper/2603.12579/) (2026)
- [Shadow Removal by High-Quality Shadow Synthesis](https://mlsanity.com/paper/2212.04108/) (2022)
- [Controlling the Latent Diffusion Model for Generative Image Shadow Removal via Residual Generation](https://mlsanity.com/paper/2412.02322/) (2024)
- [FASR-Net: Unsupervised Shadow Removal Leveraging Inherent Frequency Priors](https://mlsanity.com/paper/2504.05779/) (2025)
- [DeshadowMamba: Deshadowing as 1D Sequential Similarity](https://mlsanity.com/paper/2510.24260/) (2025)
- [UnShadowNet: Illumination Critic Guided Contrastive Learning For Shadow Removal](https://mlsanity.com/paper/2203.15441/) (2022)
- [LAB-Net: LAB Color-Space Oriented Lightweight Network for Shadow Removal](https://mlsanity.com/paper/2208.13039/) (2022)

## Abstract (arXiv)

Shadow removal can significantly improve the image visual quality and has many applications in computer vision. Deep learning methods based on CNNs have become the most effective approach for shadow removal by training on either paired data, where both the shadow and underlying shadow-free versions of an image are known, or unpaired data, where shadow and shadow-free training images are totally different with no correspondence. In practice, CNN training on unpaired data is more preferred given the easiness of training data collection. In this paper, we present a new Lightness-Guided Shadow Removal Network (LG-ShadowNet) for shadow removal by training on unpaired data. In this method, we first train a CNN module to compensate for the lightness and then train a second CNN module with the guidance of lightness information from the first CNN module for final shadow removal. We also introduce a loss function to further utilise the colour prior of existing data. Extensive experiments on widely used ISTD, adjusted ISTD and USR datasets demonstrate that the proposed method outperforms the state-of-the-art methods with training on unpaired data.