Shadow Detection And Removal

7 papers with code • 0 benchmarks • 0 datasets

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Most implemented papers

Direction-aware Spatial Context Features for Shadow Detection and Removal

xw-hu/DSC 12 May 2018

This paper presents a novel deep neural network design for shadow detection and removal by analyzing the spatial image context in a direction-aware manner.

SpA-Former: Transformer image shadow detection and removal via spatial attention

zhangbaijin/spa-former-shadow-removal 22 Jun 2022

In this paper, we propose an end-to-end SpA-Former to recover a shadow-free image from a single shaded image.

Conditional GANs for Multi-Illuminant Color Constancy: Revolution or Yet Another Approach?

acecreamu/angularGAN 15 Nov 2018

Non-uniform and multi-illuminant color constancy are important tasks, the solution of which will allow to discard information about lighting conditions in the image.

Local Water-Filling Algorithm for Shadow Detection and Removal of Document Images

BingshuCV/DocumentShadowRemoval Sensors 2020

The proposed method can remove the shading artifacts and outperform some state-of-the-art methods, especially for the removal of shadow boundaries.

Learning from Synthetic Shadows for Shadow Detection and Removal

naoto0804/SynShadow 5 Jan 2021

To overcome this challenge, we present SynShadow, a novel large-scale synthetic shadow/shadow-free/matte image triplets dataset and a pipeline to synthesize it.

SAM-helps-Shadow:When Segment Anything Model meet shadow removal

zhangbaijin/sam-helps-shadow 1 Jun 2023

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.

NTIRE 2023 Image Shadow Removal Challenge Technical Report: Team IIM_TTI

Yuki-11/NTIRE2023_ShadowRemoval_IIM_TTI 13 Mar 2024

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.