Epitomic image factorization via neighbor-embedding

Abstract : We describe a novel epitomic image representation scheme that factors a given image content into a condensed epitome and a low-resolution image to reduce the memory space for images. Given an input image, we construct a condensed epitome such that all image patches can successfully be reconstructed from the factored representation by means of an optimized neighbor-embedding strategy. Under this new scope of epitomic image representations aligned with the manifold sampling assumption, we end up a more generic epitome learning scheme with increased optimality, compactness, and reconstruction stability. We present the performance of the proposed method for image and video up-scaling (super-resolution) while extensions to other image and video processing are straightforward.
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Communication dans un congrès
2015 IEEE International Conference on Image Processing (IEEE-ICIP), Sep 2015, Quebec City, Canada
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  • HAL Id : hal-01204755, version 1

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Mehmet Turkan, Martin Alain, Dominique Thoreau, Philippe Guillotel, Christine Guillemot. Epitomic image factorization via neighbor-embedding. 2015 IEEE International Conference on Image Processing (IEEE-ICIP), Sep 2015, Quebec City, Canada. 〈hal-01204755〉

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