On Finding Gray Pixels
About
We propose a novel grayness index for finding gray pixels and demonstrate its effectiveness and efficiency in illumination estimation. The grayness index, GI in short, is derived using the Dichromatic Reflection Model and is learning-free. GI allows to estimate one or multiple illumination sources in color-biased images. On standard single-illumination and multiple-illumination estimation benchmarks, GI outperforms state-of-the-art statistical methods and many recent deep methods. GI is simple and fast, written in a few dozen lines of code, processing a 1080p image in ~0.4 seconds with a non-optimized Matlab code.
Yanlin Qian, Joni-Kristian K\"am\"ar\"ainen, Jarno Nikkanen, Jiri Matas• 2019
Related benchmarks
| Task | Dataset | Result | Rank | |
|---|---|---|---|---|
| Color Constancy | NCC (in-dataset) | Median Error3.13 | 29 | |
| Color Constancy | LEVI (in-dataset) | Median Error3.1 | 24 | |
| Color Constancy | Gehler-Shi | Median Error1.87 | 22 | |
| Illuminant Estimation | Gehler-Shi (test) | Mean Error3.07 | 21 | |
| Illuminant Estimation | NUS-8 (test) | Mean Error2.91 | 21 | |
| Illuminant Estimation (Recovery) | ColorChecker REC (test) | Median Error1.91 | 20 | |
| Illuminant Estimation | Intel_TAU (full dataset) | Recovery Median Error2.46 | 20 | |
| Color Constancy | ETH3D RAW | Mean Error3.26 | 19 | |
| Color Constancy | DepthAWB | Mean Error3.91 | 19 | |
| Color Constancy | NYU-v2 & Diode | Mean Angular Error4.28 | 19 |
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