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Small convolution kernels for high-fidelity image restorationAn algorithm is developed for computing the mean-square-optimal values for small, image-restoration kernels. The algorithm is based on a comprehensive, end-to-end imaging system model that accounts for the important components of the imaging process: the statistics of the scene, the point-spread function of the image-gathering device, sampling effects, noise, and display reconstruction. Subject to constraints on the spatial support of the kernel, the algorithm generates the kernel values that restore the image with maximum fidelity, that is, the kernel minimizes the expected mean-square restoration error. The algorithm is consistent with the derivation of the spatially unconstrained Wiener filter, but leads to a small, spatially constrained kernel that, unlike the unconstrained filter, can be efficiently implemented by convolution. Simulation experiments demonstrate that for a wide range of imaging systems these small kernels can restore images with fidelity comparable to images restored with the unconstrained Wiener filter.
Document ID
19920029700
Acquisition Source
Legacy CDMS
Document Type
Reprint (Version printed in journal)
External Source(s)
Authors
Reichenbach, Stephen E.
(Nebraska, University Lincoln, United States)
Park, Stephen K.
(College of William and Mary Williamsburg, VA, United States)
Date Acquired
August 15, 2013
Publication Date
October 1, 1991
Publication Information
Publication: IEEE Transactions on Signal Processing
Volume: 39
ISSN: 1053-587X
Subject Category
Communications And Radar
Accession Number
92A12324
Funding Number(s)
CONTRACT_GRANT: NGT-50117
Distribution Limits
Public
Copyright
Other

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