engleski

Green Stability Assumption: Unsupervised Learning for Statistics-Based Illumination Estimation

In the image processing pipeline of almost every digital camera, there is a part for removing the influence of illumination on the colors of the image scene. Tuning the parameter values of an illumination estimation method for maximal accuracy requires calibrated images with known ground-truth illumination, but creating them for a given sensor is time-consuming. In this paper, the green stability assumption is proposed that can be used to fine-tune the values of some common illumination estimation methods by using only non-calibrated images. The obtained accuracy is practically the same as when training on calibrated images, but the whole process is much faster since calibration is not required and thus time is saved. The results are presented and discussed. The source code website is provided in Section Experimental Results.

chromaticity ; color constancy ; gray-edge ; gray-world ; green ; illumination estimation ; shades-of-gray ; standard deviation ; unsupervised learning ; white balancing

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