mlsanityData to 7 Oct 2026

ShaDocNet: Learning Spatial-Aware Tokens in Transformer for Document Shadow Removal

arXiv 2211.16675 ↗First posted 30 Nov 2022cs.CVcited by 16

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Abstract (arXiv)

Shadow removal improves the visual quality and legibility of digital copies of documents. However, document shadow removal remains an unresolved subject. Traditional techniques rely on heuristics that vary from situation to situation. Given the quality and quantity of current public datasets, the majority of neural network models are ill-equipped for this task. In this paper, we propose a Transformer-based model for document shadow removal that utilizes shadow context encoding and decoding in both shadow and shadow-free regions. Additionally, shadow detection and pixel-level enhancement are included in the whole coarse-to-fine process. On the basis of comprehensive benchmark evaluations, it is competitive with state-of-the-art methods.

Key facts. Cited by 16 papers in the map (data through 7 Oct 2026). Builds on 10 of its 19 references that are in the map. First posted on arXiv 30 Nov 2022.

Builds on

10 of 19 references.

References · 10

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  2. 2017Squeeze-and-Excitation Networkscited by 4,763
  3. 2017Stacked Conditional Generative Adversarial Networks for Jointly Learning Shadow Detection and Shadow Removalcited by 160
  4. 2019Towards Ghost-free Shadow Removal via Dual Hierarchical Aggregation Network and Shadow Matting GANcited by 102
  5. 2018Single Image Reflection Separation with Perceptual Lossescited by 98
  6. 2021Auto-Exposure Fusion for Single-Image Shadow Removalcited by 78
  7. 2021Spatial-Separated Curve Rendering Network for Efficient and High-Resolution Image Harmonizationcited by 25
  8. 2020Fine Perceptive GANs for Brain MR Image Super-Resolution in Wavelet Domaincited by 24
  9. 2019Blind Visual Motif Removal from a Single Imagecited by 18
  10. 2019Water-Filling: An Efficient Algorithm for Digitized Document Shadow Removalcited by 17

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Citing papers per year: 2023: 4; 2024: 8; 2025: 4.

16 papers.

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  1. 2023High-Resolution Document Shadow Removal via A Large-Scale Real-World Dataset and A Frequency-Aware Shadow Erasing Netcited by 36
  2. 2024DOPRA: Decoding Over-accumulation Penalization and Re-allocation in Specific Weighting Layercited by 27
  3. 2024SMAFormer: Synergistic Multi-Attention Transformer for Medical Image Segmentationcited by 14
  4. 2024Test-Time Intensity Consistency Adaptation for Shadow Detectioncited by 11
  5. 2023Top-K Pooling with Patch Contrastive Learning for Weakly-Supervised Semantic Segmentationcited by 10
  6. 2023DocDeshadower: Frequency-Aware Transformer for Document Shadow Removalcited by 9
  7. 2024Implicit Multi-Spectral Transformer: An Lightweight and Effective Visible to Infrared Image Translation Modelcited by 6
  8. 2025LensNet: An End-to-End Learning Framework for Empirical Point Spread Function Modeling and Lensless Imaging Reconstructioncited by 6
  9. 2023ShaDocFormer: A Shadow-Attentive Threshold Detector With Cascaded Fusion Refiner for Document Shadow Removalcited by 2
  10. 2024UIE-UnFold: Deep Unfolding Network with Color Priors and Vision Transformer for Underwater Image Enhancementcited by 2
  11. 2025Leveraging Contrast Information for Efficient Document Shadow Removalcited by 1
  12. 2024Medical Visual Prompting (MVP): A Unified Framework for Versatile and High-Quality Medical Image Segmentationcited by 0
4 more
  1. 2024Adaptive Query Prompting for Multi-Domain Landmark Detectioncited by 0
  2. 2024TAGE: Trustworthy Attribute Group Editing for Stable Few-shot Image Generationcited by 0
  3. 2025DocShaDiffusion: Diffusion Model in Latent Space for Document Image Shadow Removalcited by 0
  4. 2025MatteViT: High-Frequency-Aware Document Shadow Removal with Shadow Matte Guidancecited by 0