mlsanityData to 14 Oct 2026

SpA-Former: Transformer image shadow detection and removal via spatial attention

arXiv 2206.10910 ↗First posted 22 Jun 2022cs.CV cs.LGcited by 7

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Abstract (arXiv)

In this paper, we propose an end-to-end SpA-Former to recover a shadow-free image from a single shaded image. Unlike traditional methods that require two steps for shadow detection and then shadow removal, the SpA-Former unifies these steps into one, which is a one-stage network capable of directly learning the mapping function between shadows and no shadows, it does not require a separate shadow detection. Thus, SpA-former is adaptable to real image de-shadowing for shadows projected on different semantic regions. SpA-Former consists of transformer layer and a series of joint Fourier transform residual blocks and two-wheel joint spatial attention. The network in this paper is able to handle the task while achieving a very fast processing efficiency. Our code is relased on https://github.com/zhangbaijin/SpA-Former-shadow-removal

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 15 of its 22 references that are in the map. First posted on arXiv 22 Jun 2022.

Builds on

15 of 22 references.

References · 15

  1. 2021Restormer: Efficient Transformer for High-Resolution Image Restorationcited by 1,627
  2. 2017Stacked Conditional Generative Adversarial Networks for Jointly Learning Shadow Detection and Shadow Removalcited by 161
  3. 2019Towards Ghost-free Shadow Removal via Dual Hierarchical Aggregation Network and Shadow Matting GANcited by 103
  4. 2019Shadow Removal via Shadow Image Decompositioncited by 98
  5. 2019Mask-ShadowGAN: Learning to Remove Shadows from Unpaired Datacited by 92
  6. 2021Auto-Exposure Fusion for Single-Image Shadow Removalcited by 79
  7. 2020From Shadow Segmentation to Shadow Removalcited by 59
  8. 2018Direction-aware Spatial Context Features for Shadow Detection and Removalcited by 58
  9. 2021From Shadow Generation to Shadow Removalcited by 55
  10. 2021CANet: A Context-Aware Network for Shadow Removalcited by 47
  11. 2020Shadow Removal by a Lightness-Guided Network with Training on Unpaired Datacited by 38
  12. 2019RIS-GAN: Explore Residual and Illumination with Generative Adversarial Networks for Shadow Removalcited by 35
3 more
  1. 2020Cloud Removal for Remote Sensing Imagery via Spatial Attention Generative Adversarial Networkcited by 29
  2. 2020Unsupervised Shadow Removal Using Target Consistency Generative Adversarial Networkcited by 5
  3. 2020Self-Supervised Shadow Removalcited by 5

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 3; 2024: 1; 2025: 1; 2026: 2.

7 papers.

  1. 2024Towards Image Ambient Lighting Normalizationcited by 9
  2. 2023Enlighten Anything: When Segment Anything Model Meets Low-Light Image Enhancementcited by 8
  3. 2023Memory augment is All You Need for image restorationcited by 8
  4. 2023SAM-helps-Shadow:When Segment Anything Model meet shadow removalcited by 6
  5. 2026DINOLight: Robust Ambient Light Normalization with Self-supervised Visual Prior Integrationcited by 2
  6. 2025NTIRE 2025 Image Shadow Removal Challenge Reportcited by 1
  7. 2026OmniLight: One Model to Rule All Lighting Conditionscited by 0