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Super-resolution of images based on local correlations
1999
IEEE Transactions on Neural Networks
An adaptive two step paradigm for the superresolution of optical images is developed in this paper. The procedure locally projects image samples onto a family of kernels that are learned from image data. First, an unsupervised feature extraction is performed on local neighborhood information from a training image. These features are then used to cluster the neighborhoods into disjoint sets for which an optimal mapping relating homologous neighborhoods across scales can be learned in a
doi:10.1109/72.750566
pmid:18252533
fatcat:rctlrsgd5rekpmqx75layyk65y