# High-Resolution Document Shadow Removal via A Large-Scale Real-World Dataset and A Frequency-Aware Shadow Erasing Net

arXiv: https://arxiv.org/abs/2308.14221 · HTML: https://mlsanity.com/paper/2308.14221/

## Key facts

- Cited by 36 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 38 references that are in the map.
- First posted on arXiv 27 Aug 2023.

## Builds on (most cited first, 26 of 26)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Fast R-CNN](https://mlsanity.com/paper/1504.08083/) (2015)
- [Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network](https://mlsanity.com/paper/1609.04802/) (2016)
- [Multi-Scale Context Aggregation by Dilated Convolutions](https://mlsanity.com/paper/1511.07122/) (2015)
- [Uformer: A General U-Shaped Transformer for Image Restoration](https://mlsanity.com/paper/2106.03106/) (2021)
- [Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition](https://mlsanity.com/paper/1406.4729/) (2014)
- [Simple Baselines for Image Restoration](https://mlsanity.com/paper/2204.04676/) (2022)
- [LayoutLM: Pre-training of Text and Layout for Document Image Understanding](https://mlsanity.com/paper/1912.13318/) (2019)
- [An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition](https://mlsanity.com/paper/1507.05717/) (2015)
- [Ultra-High-Definition Low-Light Image Enhancement: A Benchmark and Transformer-Based Method](https://mlsanity.com/paper/2212.11548/) (2022)
- [Stacked Conditional Generative Adversarial Networks for Jointly Learning Shadow Detection and Shadow Removal](https://mlsanity.com/paper/1712.02478/) (2017)
- [Towards Ghost-free Shadow Removal via Dual Hierarchical Aggregation Network and Shadow Matting GAN](https://mlsanity.com/paper/1911.08718/) (2019)
- [Mask-ShadowGAN: Learning to Remove Shadows from Unpaired Data](https://mlsanity.com/paper/1903.10683/) (2019)
- [Auto-Exposure Fusion for Single-Image Shadow Removal](https://mlsanity.com/paper/2103.01255/) (2021)
- [Improving the Harmony of the Composite Image by Spatial-Separated Attention Module](https://mlsanity.com/paper/1907.06406/) (2019)
- [High-Resolution Photorealistic Image Translation in Real-Time: A Laplacian Pyramid Translation Network](https://mlsanity.com/paper/2105.09188/) (2021)
- [From Shadow Segmentation to Shadow Removal](https://mlsanity.com/paper/2008.00267/) (2020)
- [Physics-based Shadow Image Decomposition for Shadow Removal](https://mlsanity.com/paper/2012.13018/) (2020)
- [Shadow Removal by a Lightness-Guided Network with Training on Unpaired Data](https://mlsanity.com/paper/2006.15617/) (2020)
- [ShadowFormer: Global Context Helps Image Shadow Removal](https://mlsanity.com/paper/2302.01650/) (2023)
- [Spatial-Separated Curve Rendering Network for Efficient and High-Resolution Image Harmonization](https://mlsanity.com/paper/2109.05750/) (2021)
- [Water-Filling: An Efficient Algorithm for Digitized Document Shadow Removal](https://mlsanity.com/paper/1904.09763/) (2019)
- [ShaDocNet: Learning Spatial-Aware Tokens in Transformer for Document Shadow Removal](https://mlsanity.com/paper/2211.16675/) (2022)
- [Shadow-Aware Dynamic Convolution for Shadow Removal](https://mlsanity.com/paper/2205.04908/) (2022)

## Built on by (most cited first, 30 of 36)

- [DocRes: A Generalist Model Toward Unifying Document Image Restoration Tasks](https://mlsanity.com/paper/2405.04408/) (2024)
- [MFDNet: Multi-Frequency Deflare Network for Efficient Nighttime Flare Removal](https://mlsanity.com/paper/2406.18079/) (2024)
- [Prompt Categories Cluster for Weakly Supervised Semantic Segmentation](https://mlsanity.com/paper/2412.13823/) (2024)
- [SMAFormer: Synergistic Multi-Attention Transformer for Medical Image Segmentation](https://mlsanity.com/paper/2409.00346/) (2024)
- [Underwater Image Restoration via Polymorphic Large Kernel CNNs](https://mlsanity.com/paper/2412.18459/) (2024)
- [High-Fidelity Document Stain Removal via A Large-Scale Real-World Dataset and A Memory-Augmented Transformer](https://mlsanity.com/paper/2410.22922/) (2024)
- [Test-Time Intensity Consistency Adaptation for Shadow Detection](https://mlsanity.com/paper/2410.07695/) (2024)
- [UWFormer: Underwater Image Enhancement via a Semi-Supervised Multi-Scale Transformer](https://mlsanity.com/paper/2310.20210/) (2023)
- [Towards Image Ambient Lighting Normalization](https://mlsanity.com/paper/2403.18730/) (2024)
- [OCRGenBench: A Comprehensive Benchmark for Evaluating OCR Generative Capabilities](https://mlsanity.com/paper/2507.15085/) (2025)
- [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)
- [Implicit Multi-Spectral Transformer: An Lightweight and Effective Visible to Infrared Image Translation Model](https://mlsanity.com/paper/2404.07072/) (2024)
- [LensNet: An End-to-End Learning Framework for Empirical Point Spread Function Modeling and Lensless Imaging Reconstruction](https://mlsanity.com/paper/2505.01755/) (2025)
- [QEAN: Quaternion-Enhanced Attention Network for Visual Dance Generation](https://mlsanity.com/paper/2403.11626/) (2024)
- [Multi-modal Mood Reader: Pre-trained Model Empowers Cross-Subject Emotion Recognition](https://mlsanity.com/paper/2405.19373/) (2024)
- [ShaDocFormer: A Shadow-Attentive Threshold Detector With Cascaded Fusion Refiner for Document Shadow Removal](https://mlsanity.com/paper/2309.06670/) (2023)
- [UIE-UnFold: Deep Unfolding Network with Color Priors and Vision Transformer for Underwater Image Enhancement](https://mlsanity.com/paper/2408.10653/) (2024)
- [DLEN: Dual Branch of Transformer for Low-Light Image Enhancement in Dual Domains](https://mlsanity.com/paper/2501.12235/) (2025)
- [TextDoctor: Unified Document Image Inpainting via Patch Pyramid Diffusion Models](https://mlsanity.com/paper/2503.04021/) (2025)
- [LayeredDoc: Domain Adaptive Document Restoration with a Layer Separation Approach](https://mlsanity.com/paper/2406.08610/) (2024)
- [Leveraging Contrast Information for Efficient Document Shadow Removal](https://mlsanity.com/paper/2504.00385/) (2025)
- [Contrastive Prompt Clustering for Weakly Supervised Semantic Segmentation](https://mlsanity.com/paper/2508.17009/) (2025)
- [Medical Visual Prompting (MVP): A Unified Framework for Versatile and High-Quality Medical Image Segmentation](https://mlsanity.com/paper/2404.01127/) (2024)
- [Adaptive Query Prompting for Multi-Domain Landmark Detection](https://mlsanity.com/paper/2404.01194/) (2024)
- [Soft-Hard Attention U-Net Model and Benchmark Dataset for Multiscale Image Shadow Removal](https://mlsanity.com/paper/2408.03734/) (2024)
- [TAGE: Trustworthy Attribute Group Editing for Stable Few-shot Image Generation](https://mlsanity.com/paper/2410.17855/) (2024)
- [Single Document Image Highlight Removal via A Large-Scale Real-World Dataset and A Location-Aware Network](https://mlsanity.com/paper/2504.14238/) (2025)
- [GL-PGENet: A Parameterized Generation Framework for Robust Document Image Enhancement](https://mlsanity.com/paper/2505.22021/) (2025)
- [DocShaDiffusion: Diffusion Model in Latent Space for Document Image Shadow Removal](https://mlsanity.com/paper/2507.01422/) (2025)
- [DocIQ: A Benchmark Dataset and Feature Fusion Network for Document Image Quality Assessment](https://mlsanity.com/paper/2509.17012/) (2025)

## Abstract (arXiv)

Shadows often occur when we capture the documents with casual equipment, which influences the visual quality and readability of the digital copies. Different from the algorithms for natural shadow removal, the algorithms in document shadow removal need to preserve the details of fonts and figures in high-resolution input. Previous works ignore this problem and remove the shadows via approximate attention and small datasets, which might not work in real-world situations. We handle high-resolution document shadow removal directly via a larger-scale real-world dataset and a carefully designed frequency-aware network. As for the dataset, we acquire over 7k couples of high-resolution (2462 x 3699) images of real-world document pairs with various samples under different lighting circumstances, which is 10 times larger than existing datasets. As for the design of the network, we decouple the high-resolution images in the frequency domain, where the low-frequency details and high-frequency boundaries can be effectively learned via the carefully designed network structure. Powered by our network and dataset, the proposed method clearly shows a better performance than previous methods in terms of visual quality and numerical results. The code, models, and dataset are available at: https://github.com/CXH-Research/DocShadow-SD7K