Image interpolation algorithms try to fit a function to a matrix of samples in a "natural-looking" way. This paper presents edge inference, an algorithm that does this by mixing neural network regression with standard image interpolation techniques. Results on gray level images are presented, and it is demonstrated that edge inference is capable of producing sharp, natural-looking results. A technique for reintroducing noise is given, and it is shown that, with noise added using a bicubic interpolant, edge inference can be regarded as a generalization of bicubic interpolation. Extension into RGB color space and additional applications of the algorithm are discussed, and some tips for optimization are given.
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