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
Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement
Li-Chongyi/Zero-DCE
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CVPR 2020
The paper presents a novel method, Zero-Reference Deep Curve Estimation (Zero-DCE), which formulates light enhancement as a task of image-specific curve estimation with a deep network.
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.
Fast Fourier Color Constancy
We present Fast Fourier Color Constancy (FFCC), a color constancy algorithm which solves illuminant estimation by reducing it to a spatial localization task on a torus.
Color Constancy Using CNNs
In this work we describe a Convolutional Neural Network (CNN) to accurately predict the scene illumination.
Convolutional Color Constancy
Color constancy is the problem of inferring the color of the light that illuminated a scene, usually so that the illumination color can be removed.
FC4: Fully Convolutional Color Constancy With Confidence-Weighted Pooling
However, the patch-based CNNs that exist for this problem are faced with the issue of estimation ambiguity, where a patch may contain insufficient information to establish a unique or even a limited possible range of illumination colors.
Semantic White Balance: Semantic Color Constancy Using Convolutional Neural Network
mahmoudnafifi/Semantic-Color-Constancy-Using-CNN
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The goal of computational color constancy is to preserve the perceptive colors of objects under different lighting conditions by removing the effect of color casts caused by the scene's illumination.
Revisiting Gray Pixel for Statistical Illumination Estimation
We present a statistical color constancy method that relies on novel gray pixel detection and mean shift clustering.
Color Constancy by Reweighting Image Feature Maps
In this study, a novel illuminant color estimation framework is proposed for computational color constancy, which incorporates the high representational capacity of deep-learning-based models and the great interpretability of assumption-based models.
Decoupling Semantic Context and Color Correlation with multi-class cross branch regularization
This paper presents a novel design methodology for architecting a light-weight and faster DNN architecture for vision applications.
