NDLI: Image estimation using fast modified reduced update Kalman filter

Content Provider	IEEE Xplore Digital Library
Author	Wen-Rong Wu Kundu, A.
Copyright Year	1991
Abstract	The authors have proposed some modifications of the reduced update Kalman filter (RUKF) as applied to filtering of images corrupted by additive noise. They have reduced the computational complexity by reducing the state dimensionality. By doing so, it is shown that the computational requirement is reduced by an order of magnitude while the loss of performance is only marginal. Next, the RUKF is modified using the score function based approach to accommodate non-Gaussian noise. The image is modeled as a nonstationary mean and stationary variance autoregressive Gaussian process. It is shown that the stationary variance assumption is reasonable if the nonstationary mean is computed by an edge and detail preserving efficient estimator of local nonstationary mean. Such an estimator, called the hybrid multistage medium D (HMSMD) filter, is also described. Detailed experimental results are provided which indicate the success of the new filtering scheme.<>
Sponsorship	IEEE Signal Processing Society
Starting Page	915
Ending Page	926
Page Count	12
File Size	1280282
File Format	PDF
ISSN	1053587X
Volume Number	40
Issue Number	4
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1992-04-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Filters Filtering Additive noise Gaussian processes Humans Computational complexity Performance loss Shape Histograms
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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