Sparse Image Representation with Epitomes

13 years 15 days ago

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Sparse coding, which is the decomposition of a vector using only a few basis elements, is widely used in machine learning and image processing. The basis set, also called dictionary, is learned to adapt to speciﬁc data. This approach has proven to be very effective in many image processing tasks. Traditionally, the dictionary is an unstructured “ﬂat” set of atoms. In this paper, we study structured dictionaries [1] which are obtained from an epitome [11], or a set of epitomes. The epitome is itself a small image, and the atoms are all the patches of a chosen size inside this image. This considerably reduces the number of parameters to learn and provides sparse image decompositions with shiftinvariance properties. We propose a new formulation and an algorithm for learning the structured dictionaries associated with epitomes, and illustrate their use in image denoising tasks.

Louise Benoit, Julien Mairal, Francis Bach, Jean P

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