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<p>Due to substantial fluctuations in brightness at shadow boundaries, existing shadow removal algorithms often struggle to accurately eliminate these boundaries. This challenge is further compounded by the reliance on binary mask representations for shadow regions. Therefore, we propose a novel approach to shadow removal using 2D Signed Distance Field (SDF), which serves as a smooth weight prior to handle shadow boundaries more effectively. First, we introduce a fast Fourier transform (FFT) framework to capture both global frequency and local spatial features, enhancing the overall quality of shadow removal. Additionally, we propose an information interaction module (IIM) to fuse local spatial information with global frequency information obtained from the FFT, thereby improving the precision of shadow boundary handling. Second, we specially design a boundary refinement module (BRM) for shadow boundaries, leveraging the characteristics of the SDF to ensure smoother and more natural elimination of shadow boundaries. Finally, we introduce a global feature modulation technique to combine features from SDF, FFT, and non-shadow regions, further enhancing the overall shadow removal results. Extensive experiments demonstrate that our method is comparable to state-of-the-art approaches, particularly in effectively removing shadow boundaries and achieving high-quality results.</p>

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Single image shadow removal using 2D signed distance field

  • 期刊论文

  • 发表日期：

    2025年6月25日

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  • Yanping Fu,
  • Yuting Zhang,
  • Dengdi Sun,
  • Shaojie Zhang,
  • Haifeng Zhao

摘要

Due to substantial fluctuations in brightness at shadow boundaries, existing shadow removal algorithms often struggle to accurately eliminate these boundaries. This challenge is further compounded by the reliance on binary mask representations for shadow regions. Therefore, we propose a novel approach to shadow removal using 2D Signed Distance Field (SDF), which serves as a smooth weight prior to handle shadow boundaries more effectively. First, we introduce a fast Fourier transform (FFT) framework to capture both global frequency and local spatial features, enhancing the overall quality of shadow removal. Additionally, we propose an information interaction module (IIM) to fuse local spatial information with global frequency information obtained from the FFT, thereby improving the precision of shadow boundary handling. Second, we specially design a boundary refinement module (BRM) for shadow boundaries, leveraging the characteristics of the SDF to ensure smoother and more natural elimination of shadow boundaries. Finally, we introduce a global feature modulation technique to combine features from SDF, FFT, and non-shadow regions, further enhancing the overall shadow removal results. Extensive experiments demonstrate that our method is comparable to state-of-the-art approaches, particularly in effectively removing shadow boundaries and achieving high-quality results.