Abstract
Image pixel prediction is a very important task in image processing. To model images, many researchers use Gaussian distributions. However, some data are obviously nonGaussian, such as the image clutter and texture. In such cases, predictors are hard to derive and to obtain. In this paper, we analytically derive a new non-linear predictor based on inverted Dirichlet mixture. The non-linear combination of the neighbouring pixels and the combination of the mixture parameters demonstrate a good efficiency in predicting pixels. In order to prove the efficacy of our predictor, we use two challenging tasks, which are; object detection and image restoration.
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