Abstract
In this chapter, ensemble learning is applied to the problem of blind source separation and deconvolution of images. It is assumed that the observed images were constructed by mixing a set of images (consisting of independent, identically distributed pixels), convolving the mixtures with unknown blurring filters and then adding Gaussian noise.
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