Color Constancy
33 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
Latest papers
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).
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.
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.
STAR: A Structure-Aware Lightweight Transformer for Real-Time Image Enhancement
zzyfd/STAR-pytorch
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ICCV 2021
STAR is a general architecture that can be easily adapted to different image enhancement tasks.
Deep Neural Models for color discrimination and color constancy
AlbanFlachot/color_constancy
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High levels of color constancy were achieved with different DNN architectures.
Cross-Camera Convolutional Color Constancy
mahmoudnafifi/C5
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ICCV 2021
We present "Cross-Camera Convolutional Color Constancy" (C5), a learning-based method, trained on images from multiple cameras, that accurately estimates a scene's illuminant color from raw images captured by a new camera previously unseen during training.
The Cube++ Illumination Estimation Dataset
In this paper, a new illumination estimation dataset is proposed that aims to alleviate many of the mentioned problems and to help the illumination estimation research.
An Improved Air-Light Estimation Scheme for Single Haze Images Using Color Constancy Prior
Hazy environment attenuates the scene radiance and causes difficulty in distinguishing the color and texture of the scene.
Multi-Domain Learning for Accurate and Few-Shot Color Constancy
Given a new unseen device with limited number of training samples, our method is capable of delivering accurate color constancy by merely learning the camera-specific parameters from the few-shot dataset.
A Benchmark for Temporal Color Constancy
matteo-rizzo/saliency-faithfulness-eval
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The conventional approach is to use a single frame - shot frame - to estimate the scene illumination color.
