Wavelet priors for multiframe image restoration

Premchandra Shankar, Mark A Neifeld

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

It is known that the distributions of wavelet coefficients of natural images at different scales and orientations can be approximated by generalized Gaussian probability density functions. We exploit this prior knowledge within a novel statistical framework for multi-frame image restoration based on the maximum a-posteriori (MAP) algorithm. We describe an iterative algorithm for obtaining a high-fidelity object estimate from multiple warped, blurred, and noisy low-resolution images. We compare our new method with several other techniques including linear restoration, and restoration using Markov Random Field (MRF) object priors. We will discuss the performances of the algorithms.

Original languageEnglish (US)
Title of host publicationProceedings of SPIE - The International Society for Optical Engineering
Volume6575
DOIs
Publication statusPublished - 2007
EventVisual Information Processing XVI - Orlando, FL, United States
Duration: Apr 10 2007Apr 10 2007

Other

OtherVisual Information Processing XVI
CountryUnited States
CityOrlando, FL
Period4/10/074/10/07

    Fingerprint

Keywords

  • Multiframe image restoration
  • Optimal regularization parameters
  • Superresolution
  • Wavelet priors

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Condensed Matter Physics

Cite this

Shankar, P., & Neifeld, M. A. (2007). Wavelet priors for multiframe image restoration. In Proceedings of SPIE - The International Society for Optical Engineering (Vol. 6575). [65750D] https://doi.org/10.1117/12.720939