Shadow Removal
55 papers with code • 3 benchmarks • 6 datasets
Remove shadow from background
Benchmarks
These leaderboards are used to track progress in Shadow Removal
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ISTD
SRD
Kligler
Jung
Doc3DShade
SD7K
Latest papers
NTIRE 2023 Image Shadow Removal Challenge Technical Report: Team IIM_TTI
Yuki-11/NTIRE2023_ShadowRemoval_IIM_TTI
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In this paper, we analyze and discuss ShadowFormer in preparation for the NTIRE2023 Shadow Removal Challenge [1], implementing five key improvements: image alignment, the introduction of a perceptual quality loss function, the semi-automatic annotation for shadow detection, joint learning of shadow detection and removal, and the introduction of new data augmentation technique "CutShadow" for shadow removal.
Controlling Vision-Language Models for Multi-Task Image Restoration
In this paper, we present a degradation-aware vision-language model (DA-CLIP) to better transfer pretrained vision-language models to low-level vision tasks as a multi-task framework for image restoration.
ShaDocFormer: A Shadow-Attentive Threshold Detector With Cascaded Fusion Refiner for Document Shadow Removal
The STD module employs a traditional thresholding technique and leverages the attention mechanism of the Transformer to gather global information, thereby enabling precise detection of shadow masks.
High-Resolution Document Shadow Removal via A Large-Scale Real-World Dataset and A Frequency-Aware Shadow Erasing Net
CXH-Research/DocShadow-SD7K
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ICCV 2023
We handle high-resolution document shadow removal directly via a larger-scale real-world dataset and a carefully designed frequency-aware network.
A Shadow Imaging Bilinear Model and Three-branch Residual Network for Shadow Removal
nachifur/TBRNet
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IEEE Transactions on Neural Networks and Learning Systems 2023
Thus, our network ensures the fidelity of nonshadow areas and restores the light intensity of shadow areas through three-branch collaboration.
SAM-helps-Shadow:When Segment Anything Model meet shadow removal
The challenges surrounding the application of image shadow removal to real-world images and not just constrained datasets like ISTD/SRD have highlighted an urgent need for zero-shot learning in this field.
SIDAR: Synthetic Image Dataset for Alignment & Restoration
Our data generation pipeline is customizable and can be applied to any existing dataset, serving as a data augmentation to further improve the feature learning of any existing method.
Shadow Removal of Text Document Images Using Background Estimation and Adaptive Text Enhancement
Thirdly, we propose an adaptive text contrast enhancement strategy to generate shadow-free results with comfortable visual perception across shadow and non-shadow regions.
Refusion: Enabling Large-Size Realistic Image Restoration with Latent-Space Diffusion Models
algolzw/image-restoration-sde
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This work aims to improve the applicability of diffusion models in realistic image restoration.
A Decoupled Multi-Task Network for Shadow Removal
nachifur/DMTN
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IEEE Transactions on Multimedia 2023
Last, these features are converted to a target shadow-free image, affiliated shadow matte, and shadow image, supervised by multi-task joint loss functions.
