Shadow Removal
58 papers with code • 3 benchmarks • 6 datasets
Remove shadow from background
Benchmarks
These leaderboards are used to track progress in Shadow Removal
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
|||||
|
|||||
|
ISTD
SRD
Kligler
Jung
Doc3DShade
SD7K
Latest papers with no code
Accurate Tennis Court Line Detection on Amateur Recorded Matches
Typically, tennis court line detection is done by running Hough-Line-Detection to find straight lines in the image, and then computing a transformation matrix from the detected lines to create the final court structure.
Towards Image Ambient Lighting Normalization
However, existing works often simplify this task within the context of shadow removal, limiting the light sources to one and oversimplifying the scene, thus excluding complex self-shadows and restricting surface classes to smooth ones.
Benchmarking Adversarial Robustness of Image Shadow Removal with Shadow-adaptive Attacks
This is primarily due to the unique characteristic of spatially varying illumination within shadow images.
ShadowRemovalNet: Efficient Real-Time Shadow Removal
We propose ShadowRemovalNet, a novel method designed for real-time image processing on resource-constrained hardware.
Recasting Regional Lighting for Shadow Removal
Based on this observation, we propose to condition the restoration of attenuated textures on the corrected local lighting in the shadow region.
Neural Spline Fields for Burst Image Fusion and Layer Separation
Each photo in an image burst can be considered a sample of a complex 3D scene: the product of parallax, diffuse and specular materials, scene motion, and illuminant variation.
Latent Feature-Guided Diffusion Models for Shadow Removal
Recovering textures under shadows has remained a challenging problem due to the difficulty of inferring shadow-free scenes from shadow images.
DECDM: Document Enhancement using Cycle-Consistent Diffusion Models
The performance of optical character recognition (OCR) heavily relies on document image quality, which is crucial for automatic document processing and document intelligence.
Progressive Recurrent Network for Shadow Removal
To handle this issue, we consider removing the shadow in a coarse-to-fine fashion and propose a simple but effective Progressive Recurrent Network (PRNet).
Deshadow-Anything: When Segment Anything Model Meets Zero-shot shadow removal
Segment Anything (SAM), an advanced universal image segmentation model trained on an expansive visual dataset, has set a new benchmark in image segmentation and computer vision.
