# Shadow-Aware Dynamic Convolution for Shadow Removal arXiv: https://arxiv.org/abs/2205.04908 ยท HTML: https://mlsanity.com/paper/2205.04908/ ## Key facts - Cited by 11 papers in the map (data through 14 Oct 2026). - Builds on 29 of its 52 references that are in the map. - First posted on arXiv 10 May 2022. ## Builds on (most cited first, 29 of 29) - [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015) - [SwinIR: Image Restoration Using Swin Transformer](https://mlsanity.com/paper/2108.10257/) (2021) - [Restormer: Efficient Transformer for High-Resolution Image Restoration](https://mlsanity.com/paper/2111.09881/) (2021) - [Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey](https://mlsanity.com/paper/1801.00553/) (2018) - [Dynamic Neural Networks: A Survey](https://mlsanity.com/paper/2102.04906/) (2021) - [Dynamic Convolution: Attention over Convolution Kernels](https://mlsanity.com/paper/1912.03458/) (2019) - [Focal Frequency Loss for Image Reconstruction and Synthesis](https://mlsanity.com/paper/2012.12821/) (2020) - [Stacked Conditional Generative Adversarial Networks for Jointly Learning Shadow Detection and Shadow Removal](https://mlsanity.com/paper/1712.02478/) (2017) - [Dynamic Channel Pruning: Feature Boosting and Suppression](https://mlsanity.com/paper/1810.05331/) (2018) - [Towards Ghost-free Shadow Removal via Dual Hierarchical Aggregation Network and Shadow Matting GAN](https://mlsanity.com/paper/1911.08718/) (2019) - [Scene Graph Generation with External Knowledge and Image Reconstruction](https://mlsanity.com/paper/1904.00560/) (2019) - [Shadow Removal via Shadow Image Decomposition](https://mlsanity.com/paper/1908.08628/) (2019) - [Blind Image Super-Resolution: A Survey and Beyond](https://mlsanity.com/paper/2107.03055/) (2021) - [Dynamic Convolutions: Exploiting Spatial Sparsity for Faster Inference](https://mlsanity.com/paper/1912.03203/) (2019) - [Auto-Exposure Fusion for Single-Image Shadow Removal](https://mlsanity.com/paper/2103.01255/) (2021) - [Attention Guided Low-light Image Enhancement with a Large Scale Low-light Simulation Dataset](https://mlsanity.com/paper/1908.00682/) (2019) - [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) - [Spatially Adaptive Inference with Stochastic Feature Sampling and Interpolation](https://mlsanity.com/paper/2003.08866/) (2020) - [CANet: A Context-Aware Network for Shadow Removal](https://mlsanity.com/paper/2108.09894/) (2021) - [Revisiting Dynamic Convolution via Matrix Decomposition](https://mlsanity.com/paper/2103.08756/) (2021) - [Improving Shadow Suppression for Illumination Robust Face Recognition](https://mlsanity.com/paper/1710.05073/) (2017) - [Image Captioning with Deep Bidirectional LSTMs](https://mlsanity.com/paper/1604.00790/) (2016) - [Generalizable Model-agnostic Semantic Segmentation via Target-specific Normalization](https://mlsanity.com/paper/2003.12296/) (2020) - [Interactive Removal and Ground Truth for Difficult Shadow Scenes](https://mlsanity.com/paper/1608.00762/) (2016) - [A Black-box Adversarial Attack Strategy with Adjustable Sparsity and Generalizability for Deep Image Classifiers](https://mlsanity.com/paper/2004.13002/) (2020) - [Integrated Generalized Zero-Shot Learning for Fine-Grained Classification](https://mlsanity.com/paper/2101.02141/) (2020) - [From General to Specific: Online Updating for Blind Super-Resolution](https://mlsanity.com/paper/2107.02398/) (2021) - [A Feature Consistency Driven Attention Erasing Network for Fine-Grained Image Retrieval](https://mlsanity.com/paper/2110.04479/) (2021) ## Built on by (most cited first, 11 of 11) - [High-Resolution Document Shadow Removal via A Large-Scale Real-World Dataset and A Frequency-Aware Shadow Erasing Net](https://mlsanity.com/paper/2308.14221/) (2023) - [A Large-scale Film Style Dataset for Learning Multi-frequency Driven Film Enhancement](https://mlsanity.com/paper/2301.08880/) (2023) - [ShadowRefiner: Towards Mask-free Shadow Removal via Fast Fourier Transformer](https://mlsanity.com/paper/2406.02559/) (2024) - [ShadowMaskFormer: Mask Augmented Patch Embeddings for Shadow Removal](https://mlsanity.com/paper/2404.18433/) (2024) - [SAM-helps-Shadow:When Segment Anything Model meet shadow removal](https://mlsanity.com/paper/2306.06113/) (2023) - [Timeline and Boundary Guided Diffusion Network for Video Shadow Detection](https://mlsanity.com/paper/2408.11785/) (2024) - [Retinex-guided Histogram Transformer for Mask-free Shadow Removal](https://mlsanity.com/paper/2504.14092/) (2025) - [BurstDeflicker: A Benchmark Dataset for Flicker Removal in Dynamic Scenes](https://mlsanity.com/paper/2510.09996/) (2025) - [Structure-Informed Shadow Removal Networks](https://mlsanity.com/paper/2301.03182/) (2023) - [Cross-Modal Spherical Aggregation for Weakly Supervised Remote Sensing Shadow Removal](https://mlsanity.com/paper/2406.17469/) (2024) - [UniBlendNet: Unified Global, Multi-Scale, and Region-Adaptive Modeling for Ambient Lighting Normalization](https://mlsanity.com/paper/2604.13383/) (2026) ## Abstract (arXiv) With a wide range of shadows in many collected images, shadow removal has aroused increasing attention since uncontaminated images are of vital importance for many downstream multimedia tasks. Current methods consider the same convolution operations for both shadow and non-shadow regions while ignoring the large gap between the color mappings for the shadow region and the non-shadow region, leading to poor quality of reconstructed images and a heavy computation burden. To solve this problem, this paper introduces a novel plug-and-play Shadow-Aware Dynamic Convolution (SADC) module to decouple the interdependence between the shadow region and the non-shadow region. Inspired by the fact that the color mapping of the non-shadow region is easier to learn, our SADC processes the non-shadow region with a lightweight convolution module in a computationally cheap manner and recovers the shadow region with a more complicated convolution module to ensure the quality of image reconstruction. Given that the non-shadow region often contains more background color information, we further develop a novel intra-convolution distillation loss to strengthen the information flow from the non-shadow region to the shadow region. Extensive experiments on the ISTD and SRD datasets show our method achieves better performance in shadow removal over many state-of-the-arts. Our code is available at https://github.com/xuyimin0926/SADC.