Wavelet priors for multiframe image restoration

Premchandra Shankar, Mark Neifeld

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


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 publicationVisual Informaion Processing XVI
StatePublished - 2007
Externally publishedYes
EventVisual Information Processing XVI - Orlando, FL, United States
Duration: Apr 10 2007Apr 10 2007

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
ISSN (Print)0277-786X


OtherVisual Information Processing XVI
Country/TerritoryUnited States
CityOrlando, FL


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

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering


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