NDLI: Superresolution and noise filtering using moving least squares

Content Provider	IEEE Xplore Digital Library
Author	Bose, N.K. Ahuja, N.A.
Copyright Year	1992
Abstract	An irregularly spaced sampling raster formed from a sequence of low-resolution frames is the input to an image sequence superresolution algorithm whose output is the set of image intensity values at the desired high-resolution image grid. The method of moving least squares (MLS) in polynomial space has proved to be useful in filtering the noise and approximating scattered data by minimizing a weighted mean-square error norm, but introducing blur in the process. Starting with the continuous version of the MLS, an explicit expression for the filter bandwidth is obtained as a function of the polynomial order of approximation and the standard deviation (scale) of the Gaussian weight function. A discrete implementation of the MLS is performed on images and the effect of choice of the two dependent parameters, scale and order, on noise filtering and reduction of blur introduced during the MLS process is studied
Sponsorship	IEEE Signal Processing Society
Page Count	10
File Size	2222831
Starting Page	2239
Ending Page	2248
File Format	PDF
ISSN	10577149
Volume Number	15
Issue Number	8
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-08-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Filtering Least squares methods Multilevel systems Least squares approximation Polynomials Image sampling Image sequences Image resolution Scattering Mean square error methods superresolution Hermite polynomials moving least squares (MLS)
Content Type	Text
Resource Type	Article
Subject	Computer Graphics and Computer-Aided Design Software

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