## Abstract

We describe a statistical model for images decomposed in an overcomplete wavelet pyramid. Each coefficient of the pyramid is modeled as the product of two independent random variables: an element of a Gaussian random field, and a hidden multiplier with a marginal log-normal prior. The latter modulates the local variance of the coefficients. We assume subband coefficients are contaminated with additive Gaussian noise of known covariance, and compute a MAP estimate of each multiplier variable based on observation of a local neighborhood of coefficients. Conditioned on this multiplier, we then estimate the subband coefficients with a local Wiener estimator. Unlike previous approaches, we (a) empirically motivate our choice for the prior on the multiplier; (b) use the full covariance of signal and noise in the estimation; (c) include adjacent scales in the conditioning neighborhood. To our knowledge, the results are the best in the literature, both visually and in terms of squared error.

Original language | English (US) |
---|---|

Pages | 37-40 |

Number of pages | 4 |

State | Published - 2001 |

Event | IEEE International Conference on Image Processing (ICIP) - Thessaloniki, Greece Duration: Oct 7 2001 → Oct 10 2001 |

### Other

Other | IEEE International Conference on Image Processing (ICIP) |
---|---|

Country/Territory | Greece |

City | Thessaloniki |

Period | 10/7/01 → 10/10/01 |

## ASJC Scopus subject areas

- Hardware and Architecture
- Computer Vision and Pattern Recognition
- Electrical and Electronic Engineering