Learning Sparse Multiscale Image Representations

@inproceedings{Sallee2002LearningSM,
  title={Learning Sparse Multiscale Image Representations},
  author={Phil Sallee and Bruno A. Olshausen},
  booktitle={NIPS},
  year={2002}
}
We describe a method for learning sparse multiscale image representations using a sparse prior distribution over the basis function coefficients. The prior consists of a mixture of a Gaussian and a Dirac delta function, and thus encourages coefficients to have exact zero values. Coefficients for an image are computed by sampling from the resulting posterior distribution with a Gibbs sampler. The learned basis is similar to the Steerable Pyramid basis, and yields slightly higher SNR for the same… CONTINUE READING
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