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231223s1995 xx ||||| 00| ||eng c |
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|e rakwb
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|a eng
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| 100 |
1 |
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|a Brailean, J C
|e verfasserin
|4 aut
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| 245 |
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|a Simultaneous recursive displacement estimation and restoration of noisy-blurred image sequences
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|c 1995
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| 336 |
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|a Text
|b txt
|2 rdacontent
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| 337 |
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|a ohne Hilfsmittel zu benutzen
|b n
|2 rdamedia
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| 338 |
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|a Band
|b nc
|2 rdacarrier
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| 500 |
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|a Date Completed 02.10.2012
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|a Date Revised 22.02.2008
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|a published: Print
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|a Citation Status PubMed-not-MEDLINE
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| 520 |
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|a We develop a recursive model-based maximum a posteriori (MAP) estimator that simultaneously estimates the displacement vector field (DVF) and the intensity field from a noisy-blurred image sequence. Current motion-compensated spatio-temporal noise filters treat the estimation of the DVF as a preprocessing step. Generally, no attempt is made to verify the accuracy of these estimates prior to their use in the filter. By simultaneously estimating these two fields, we establish a link between the two estimators. It is through this link that the DVF estimate and its corresponding accuracy information are shared with the other intensity estimator, and vice versa. To model the DVF and the intensity field, we use coupled Gauss-Markov (CGM) models. A CGM model consists of two levels: an upper level, which is made up of several submodels with various characteristics, and a lower level or line field, which governs the transitions between the submodels. The CGM models are well suited for estimating the displacement and intensity fields since the resulting estimates preserve the boundaries between the stationary areas present in both fields. Detailed line fields are proposed for the modeling of these boundaries, which also take into account the correlations that exist between these two fields. A Kalman-type estimator results, followed by a decision criterion for choosing the appropriate set of line fields. Several experiments using noisy and noisy-blurred image sequences demonstrate the superior performance of the proposed algorithm with respect to prediction error and mean-square error
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|a Journal Article
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1 |
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|a Katsaggelos, A K
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1992
|g 4(1995), 9 vom: 15., Seite 1236-51
|w (DE-627)NLM09821456X
|x 1057-7149
|7 nnns
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| 773 |
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|g volume:4
|g year:1995
|g number:9
|g day:15
|g pages:1236-51
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|a AR
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| 952 |
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|d 4
|j 1995
|e 9
|b 15
|h 1236-51
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