Clustering of matched features and gradient matching for mixed-resolution video super-resolution

Abstract

This work presents a novel technique for image reconstruction applied to mixed-resolution video super-resolution. We segment an image into patches defined by the clustering of a vector flow generated from matching SIFT features. We reconstruct the segmented image by applying image projective transformation to a reference image. By varying the number of… (More)
DOI: 10.1109/ISCAS.2015.7168855

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