Shadow Removal
68 papers with code • 6 benchmarks • 9 datasets
Image Shadow Removal
Benchmarks
These leaderboards are used to track progress in Shadow Removal
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ISTD
SRD
Kligler
Jung
INS Dataset
SD7K
Doc3DShade
Latest papers with no code
Prompt-Aware Controllable Shadow Removal
PACSRNet consists of two key modules: a prompt-aware module that generates shadow masks for the specified subject based on the user prompt, and a shadow removal module that uses the shadow prior from the first module to restore the content in the shadowed regions.
Towards Hard and Soft Shadow Removal via Dual-Branch Separation Network and Vision Transformer
Image shadow removal is a crucial task in computer vision.
Detail-Preserving Latent Diffusion for Stable Shadow Removal
The cross-dataset evaluation further demonstrates that our method generalizes effectively to unseen data, enhancing the applicability of shadow removal methods.
Controlling the Latent Diffusion Model for Generative Image Shadow Removal via Residual Generation
Large-scale generative models have achieved remarkable advancements in various visual tasks, yet their application to shadow removal in images remains challenging.
WavShadow: Wavelet Based Shadow Segmentation and Removal
Shadow removal and segmentation remain challenging tasks in computer vision, particularly in complex real world scenarios.
ShadowMamba: State-Space Model with Boundary-Region Selective Scan for Shadow Removal
This method scans boundary regions, shadow regions, and non-shadow regions independently, bringing pixels of the same region type closer together in the long sequence, especially focusing on the local information at the boundaries, which is crucial for shadow removal.
Generative Portrait Shadow Removal
For robust and natural shadow removal, we propose to train the diffusion model with a compositional repurposing framework: a pre-trained text-guided image generation model is first fine-tuned to harmonize the lighting and color of the foreground with a background scene by using a background harmonization dataset; and then the model is further fine-tuned to generate a shadow-free portrait image via a shadow-paired dataset.
Shadow Removal Refinement via Material-Consistent Shadow Edges
The crucial contribution of this paper is to learn how to identify those shadow edges that traverse material-consistent regions and how to use them as self-supervision for shadow removal refinement during test time.
Soft-Hard Attention U-Net Model and Benchmark Dataset for Multiscale Image Shadow Removal
Effective shadow removal is pivotal in enhancing the visual quality of images in various applications, ranging from computer vision to digital photography.
Semantic-guided Adversarial Diffusion Model for Self-supervised Shadow Removal
Existing unsupervised methods have addressed the challenges of inconsistent paired data and tedious acquisition of ground-truth labels in shadow removal tasks.
