Super-resolution of images based on local correlations |
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Authors: | Candocia FM Principe JC |
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Affiliation: | Lab. of Comput. Neuroeng., Florida Univ., Gainesville, FL. |
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Abstract: | An adaptive two-step paradigm for the super-resolution 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 supervised manner. A super-resolved image is obtained through the convolution of a low-resolution test image with the established family of kernels. Results demonstrate the effectiveness of the approach. |
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