Abstract
Illumination estimation is a fundamental prerequisite for many computer vision applications. Various statistics and deep learning-based estimation methods have been proposed, and further studies are ongoing. In this study, we first perform a comparative analysis of representative statistics and deep learning-based methods and subsequently investigate combining them to improve the illumination estimation accuracy. We use hyperspectral images as the training data and support vector regression to combine the methods. Based on the results, we confirm that their combination enhances their accuracy.
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