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
Latest papers with no code
Degree-of-Linear-Polarization-Based Color Constancy
Color constancy is an essential function in digital photography and a fundamental process for many computer vision applications.
Point Cloud Color Constancy
In this paper, we present Point Cloud Color Constancy, in short PCCC, an illumination chromaticity estimation algorithm exploiting a point cloud.
Generative Models for Multi-Illumination Color Constancy
However, most of the existing color constancy methods are designed for single light sources.
Multi-color balance for color constancy
In this paper, we propose a novel multi-color balance adjustment for color constancy.
Illumination Estimation Challenge: experience of past two years
The main advantage of testing a method on a challenge over testing in on some of the known datasets is the fact that the ground-truth illuminations for the challenge test images are unknown up until the results have been submitted, which prevents any potential hyperparameter tuning that may be biased.
Underwater Image Color Correction by Complementary Adaptation
In this paper, we propose a novel approach for underwater image color correction based on a Tikhonov type optimization model in the CIELAB color space.
Shape, Illumination, and Reflectance from Shading
A fundamental problem in computer vision is that of inferring the intrinsic, 3D structure of the world from flat, 2D images of that world.
Monte Carlo Dropout Ensembles for Robust Illumination Estimation
Computational color constancy is a preprocessing step used in many camera systems.
End-to-End Illuminant Estimation Based on Deep Metric Learning
Previous deep learning approaches to color constancy usually directly estimate illuminant value from input image.
Probabilistic Color Constancy
In this paper, we propose a novel unsupervised color constancy method, called Probabilistic Color Constancy (PCC).
