Revisiting Gray Pixel for Statistical Illumination Estimation
This work addresses color constancy for image processing, presenting an incremental improvement with specific gains in statistical methods.
The authors tackled the problem of statistical color constancy by developing a method based on gray pixel detection and mean shift clustering, which outperforms state-of-the-art methods in camera-agnostic scenarios and all statistical methods when the camera is known.
We present a statistical color constancy method that relies on novel gray pixel detection and mean shift clustering. The method, called Mean Shifted Grey Pixel -- MSGP, is based on the observation: true-gray pixels are aligned towards one single direction. Our solution is compact, easy to compute and requires no training. Experiments on two real-world benchmarks show that the proposed approach outperforms state-of-the-art methods in the camera-agnostic scenario. In the setting where the camera is known, MSGP outperforms all statistical methods.