Color Constancy
35 papers with code • 1 benchmarks • 5 datasets
Color Constancy is the ability of the human vision system to perceive the colors of the objects in the scene largely invariant to the color of the light source. The task of computational Color Constancy is to estimate the scene illumination and then perform the chromatic adaptation in order to remove the influence of the illumination color on the colors of the objects in the scene.
Benchmarks
These leaderboards are used to track progress in Color Constancy
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
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INTEL-TAU
VegFru
Rendered WB dataset
Cube++
LSMI
Most implemented papers
CLCC: Contrastive Learning for Color Constancy
howardyclo/clcc-cvpr21
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CVPR 2021
In this paper, we present CLCC, a novel contrastive learning framework for color constancy.
Robust channel-wise illumination estimation
firasl/CWCC
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We test this approach on the proposed method and show that it can indeed be used to avoid several extreme error cases and, thus, improves the practicality of the proposed technique.
Model-Based Image Signal Processors via Learnable Dictionaries
mv-lab/AISP
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Digital cameras transform sensor RAW readings into RGB images by means of their Image Signal Processor (ISP).
Self-Supervised Learning of Color Constancy
trieschlab/colorconstancylearning
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Color constancy (CC) describes the ability of the visual system to perceive an object as having a relatively constant color despite changes in lighting conditions.
Simple Image Signal Processing using Global Context Guidance
mv-lab/AISP
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First, we propose a novel module that can be integrated into any neural ISP to capture the global context information from the full RAW images.
