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
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/faithful-attention-eval
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The conventional approach is to use a single frame - shot frame - to estimate the scene illumination color.
A Multi-Hypothesis Approach to Color Constancy
huawei-noah/multi_hyp_cc
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CVPR 2020
Firstly, we select a set of candidate scene illuminants in a data-driven fashion and apply them to a target image to generate of set of corrected images.
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.
Cascading Convolutional Color Constancy
yhlscut/C4
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Regressing the illumination of a scene from the representations of object appearances is popularly adopted in computational color constancy.
What Else Can Fool Deep Learning? Addressing Color Constancy Errors on Deep Neural Network Performance
mahmoudnafifi/WB_color_augmenter
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ICCV 2019
There is active research targeting local image manipulations that can fool deep neural networks (DNNs) into producing incorrect results.
INTEL-TAU: A Color Constancy Dataset
firasl/BoCF
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In this paper, we describe a new large dataset for illumination estimation.
Bag of Color Features For Color Constancy
firasl/BoCF
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To further improve the illumination estimation accuracy, we propose a novel attention mechanism for the BoCF model with two variants based on self-attention.
When Color Constancy Goes Wrong: Correcting Improperly White-Balanced Images
The challenge lies not in identifying what the correct white balance should have been, but in the fact that the in-camera white-balance procedure is followed by several camera-specific nonlinear color manipulations that make it challenging to correct the image's colors in post-processing.
Quasi-Unsupervised Color Constancy
claudio-unipv/quasi-unsupervised-cc
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CVPR 2019
After training, unbalanced images can be processed thanks to the preliminary conversion to grayscale of the input to the neural network.
