# Diff-Shadow: Global-guided Diffusion Model for Shadow Removal arXiv: https://arxiv.org/abs/2407.16214 ยท HTML: https://mlsanity.com/paper/2407.16214/ ## Key facts - Cited by 10 papers in the map (data through 14 Oct 2026). - Placed in 3D, point cloud and scene generation, one of 14 directions in Diffusion models. - Builds on 34 of its 50 references that are in the map. - First posted on arXiv 23 Jul 2024. ## Placed in - Diffusion models / [3D, point cloud and scene generation](https://mlsanity.com/diffusion/3d-point-cloud-and-scene-generation/) ## Builds on (most cited first, 30 of 34) - [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017) - [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020) - [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021) - [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021) - [Generative Modeling by Estimating Gradients of the Data Distribution](https://mlsanity.com/paper/1907.05600/) (2019) - [Improved Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2102.09672/) (2021) - [Image Super-Resolution via Iterative Refinement](https://mlsanity.com/paper/2104.07636/) (2021) - [RePaint: Inpainting using Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2201.09865/) (2022) - [Palette: Image-to-Image Diffusion Models](https://mlsanity.com/paper/2111.05826/) (2021) - [Improved Techniques for Training Score-Based Generative Models](https://mlsanity.com/paper/2006.09011/) (2020) - [SRDiff: Single Image Super-Resolution with Diffusion Probabilistic Models](https://mlsanity.com/paper/2104.14951/) (2021) - [Deblurring via Stochastic Refinement](https://mlsanity.com/paper/2112.02475/) (2021) - [Implicit Diffusion Models for Continuous Super-Resolution](https://mlsanity.com/paper/2303.16491/) (2023) - [Restoring Vision in Adverse Weather Conditions with Patch-Based Denoising Diffusion Models](https://mlsanity.com/paper/2207.14626/) (2022) - [Stacked Conditional Generative Adversarial Networks for Jointly Learning Shadow Detection and Shadow Removal](https://mlsanity.com/paper/1712.02478/) (2017) - [Low-Light Image Enhancement with Wavelet-based Diffusion Models](https://mlsanity.com/paper/2306.00306/) (2023) - [Parallel Diffusion Models of Operator and Image for Blind Inverse Problems](https://mlsanity.com/paper/2211.10656/) (2022) - [Towards Ghost-free Shadow Removal via Dual Hierarchical Aggregation Network and Shadow Matting GAN](https://mlsanity.com/paper/1911.08718/) (2019) - [ShadowDiffusion: When Degradation Prior Meets Diffusion Model for Shadow Removal](https://mlsanity.com/paper/2212.04711/) (2022) - [Shadow Removal via Shadow Image Decomposition](https://mlsanity.com/paper/1908.08628/) (2019) - [Auto-Exposure Fusion for Single-Image Shadow Removal](https://mlsanity.com/paper/2103.01255/) (2021) - [DC-ShadowNet: Single-Image Hard and Soft Shadow Removal Using Unsupervised Domain-Classifier Guided Network](https://mlsanity.com/paper/2207.10434/) (2022) - [Towards Coherent Image Inpainting Using Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2304.03322/) (2023) - [From Shadow Segmentation to Shadow Removal](https://mlsanity.com/paper/2008.00267/) (2020) - [Direction-aware Spatial Context Features for Shadow Detection and Removal](https://mlsanity.com/paper/1805.04635/) (2018) - [ResDiff: Combining CNN and Diffusion Model for Image Super-Resolution](https://mlsanity.com/paper/2303.08714/) (2023) - [CANet: A Context-Aware Network for Shadow Removal](https://mlsanity.com/paper/2108.09894/) (2021) - [Patched Denoising Diffusion Models For High-Resolution Image Synthesis](https://mlsanity.com/paper/2308.01316/) (2023) - [Multi-stage image denoising with the wavelet transform](https://mlsanity.com/paper/2209.12394/) (2022) - [RIS-GAN: Explore Residual and Illumination with Generative Adversarial Networks for Shadow Removal](https://mlsanity.com/paper/1911.09178/) (2019) ## Built on by (most cited first, 10 of 10) - [Detail-Preserving Latent Diffusion for Stable Shadow Removal](https://mlsanity.com/paper/2412.17630/) (2024) - [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) - [Prompt-Aware Controllable Shadow Removal](https://mlsanity.com/paper/2501.15043/) (2025) - [TS-Diff: Two-Stage Diffusion Model for Low-Light RAW Image Enhancement](https://mlsanity.com/paper/2505.04281/) (2025) - [DMAligner: Enhancing Image Alignment via Diffusion Model Based View Synthesis](https://mlsanity.com/paper/2602.23022/) (2026) - [Contrast-Prior Enhanced Duality for Mask-Free Shadow Removal](https://mlsanity.com/paper/2507.21949/) (2025) - [Embedding Physical Reasoning into Diffusion-Based Shadow Generation Under the Sun and Sky](https://mlsanity.com/paper/2512.06174/) (2025) - [WildShadowRemover: In-the-Wild Video Shadow Removal via Detail-Preserving Video Diffusion Models](https://mlsanity.com/paper/2607.26203/) (2026) - [FreeShadow: Training-Free Shadow Removal via Illumination Transfer and Selective Content Preservation in Diffusion Models](https://mlsanity.com/paper/2607.26715/) (2026) - [Consistency as Regularization for Unsupervised Shadow Removal](https://mlsanity.com/paper/2609.01806/) (2026) ## Abstract (arXiv) We propose Diff-Shadow, a global-guided diffusion model for shadow removal. Previous transformer-based approaches can utilize global information to relate shadow and non-shadow regions but are limited in their synthesis ability and recover images with obvious boundaries. In contrast, diffusion-based methods can generate better content but they are not exempt from issues related to inconsistent illumination. In this work, we combine the advantages of diffusion models and global guidance to achieve shadow-free restoration. Specifically, we propose a parallel UNets architecture: 1) the local branch performs the patch-based noise estimation in the diffusion process, and 2) the global branch recovers the low-resolution shadow-free images. A Reweight Cross Attention (RCA) module is designed to integrate global contextual information of non-shadow regions into the local branch. We further design a Global-guided Sampling Strategy (GSS) that mitigates patch boundary issues and ensures consistent illumination across shaded and unshaded regions in the recovered image. Comprehensive experiments on datasets ISTD, ISTD+, and SRD have demonstrated the effectiveness of Diff-Shadow. Compared to state-of-the-art methods, our method achieves a significant improvement in terms of PSNR, increasing from 32.33dB to 33.69dB on the ISTD dataset.