# Interactive Removal and Ground Truth for Difficult Shadow Scenes

arXiv: https://arxiv.org/abs/1608.00762 · HTML: https://mlsanity.com/paper/1608.00762/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Aug 2016.

## Built on by (most cited first, 11 of 11)

- [Auto-Exposure Fusion for Single-Image Shadow Removal](https://mlsanity.com/paper/2103.01255/) (2021)
- [Leveraging Inpainting for Single-Image Shadow Removal](https://mlsanity.com/paper/2302.05361/) (2023)
- [ShadowRefiner: Towards Mask-free Shadow Removal via Fast Fourier Transformer](https://mlsanity.com/paper/2406.02559/) (2024)
- [Shadow-Aware Dynamic Convolution for Shadow Removal](https://mlsanity.com/paper/2205.04908/) (2022)
- [Single-Image Shadow Removal Using Deep Learning: A Comprehensive Survey](https://mlsanity.com/paper/2407.08865/) (2024)
- [S3R-Net: A Single-Stage Approach to Self-Supervised Shadow Removal](https://mlsanity.com/paper/2404.12103/) (2024)
- [CNSNet: A Cleanness-Navigated-Shadow Network for Shadow Removal](https://mlsanity.com/paper/2209.02174/) (2022)
- [Structure-Informed Shadow Removal Networks](https://mlsanity.com/paper/2301.03182/) (2023)
- [UnShadowNet: Illumination Critic Guided Contrastive Learning For Shadow Removal](https://mlsanity.com/paper/2203.15441/) (2022)
- [Soft-Hard Attention U-Net Model and Benchmark Dataset for Multiscale Image Shadow Removal](https://mlsanity.com/paper/2408.03734/) (2024)
- [SARU: A Shadow-Aware and Removal Unified Framework for Remote Sensing Images with New Benchmarks](https://mlsanity.com/paper/2604.25432/) (2026)

## Abstract (arXiv)

A user-centric method for fast, interactive, robust and high-quality shadow removal is presented. Our algorithm can perform detection and removal in a range of difficult cases: such as highly textured and colored shadows. To perform detection an on-the-fly learning approach is adopted guided by two rough user inputs for the pixels of the shadow and the lit area. After detection, shadow removal is performed by registering the penumbra to a normalized frame which allows us efficient estimation of non-uniform shadow illumination changes, resulting in accurate and robust removal. Another major contribution of this work is the first validated and multi-scene category ground truth for shadow removal algorithms. This data set containing 186 images eliminates inconsistencies between shadow and shadow-free images and provides a range of different shadow types such as soft, textured, colored and broken shadow. Using this data, the most thorough comparison of state-of-the-art shadow removal methods to date is performed, showing our proposed new algorithm to outperform the state-of-the-art across several measures and shadow category. To complement our dataset, an online shadow removal benchmark website is also presented to encourage future open comparisons in this challenging field of research.