Affordable Access

Efficient multiframe Wiener restoration of blurred and noisy image sequences.

Authors
  • Ozkan, M K
  • Erdem, A T
  • Sezan, M I
  • Tekalp, A M
Type
Published Article
Journal
IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Publication Date
Jan 01, 1992
Volume
1
Issue
4
Pages
453–476
Identifiers
PMID: 18296179
Source
Medline
License
Unknown

Abstract

Computationally efficient multiframe Wiener filtering algorithms that account for both intraframe (spatial) and interframe (temporal) correlations are proposed for restoring image sequences that are degraded by both blur and noise. One is a general computationally efficient multiframe filter, the cross-correlated multiframe (CCMF) Wiener filter, which directly utilizes the power and cross power spectra of only NxN matrices, where N is the number of frames used in the restoration. In certain special cases the CCMF lends itself to a closed-form solution that does not involve any matrix inversion. A special case is the motion-compensated multiframe (MCMF) filter, where each frame is assumed to be a globally shifted version of the previous frame. In this case, the interframe correlations can be implicitly accounted for using the estimated motion information. Thus the MCMF filter requires neither explicit estimation of cross correlations among the frames nor matrix inversion. Performance and robustness results are given.

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