Using the Random Sprays Retinex Algorithm for Global Illumination Estimation

In this paper the use of Random Sprays Retinex (RSR) algorithm for global illumination estimation is proposed and its feasibility tested. Like other algorithms based on the Retinex model, RSR also provides local illumination estimation and brightness adjustment for each pixel and it is faster than other path-wise Retinex algorithms. As the assumption of the uniform illumination holds in many cases, it should be possible to use the mean of local illumination estimations of RSR as a global illumination estimation for images with (assumed) uniform illumination allowing also the accuracy to be easily measured. Therefore we propose a method for estimating global illumination estimation based on local RSR results. To our best knowledge this is the first time that RSR algorithm is used to obtain global illumination estimation. For our tests we use a publicly available color constancy image database for testing. The results are presented and discussed and it turns out that the proposed method outperforms many existing unsupervised color constancy algorithms. The source code is available at http://www.fer.unizg.hr/ipg/resources/color constancy/. Keywords—white balance, color constancy, Retinex, Random Sprays Retinex, sampling