Recursive filtering of images with symmetric extension

Appleton, B and Talbot, H (2005) Recursive filtering of images with symmetric extension. Signal Processing, 85 8: 1546-1556. doi:10.1016/j.sigpro.2005.02.007

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Author Appleton, B
Talbot, H
Title Recursive filtering of images with symmetric extension
Journal name Signal Processing   Check publisher's open access policy
ISSN 0165-1684
Publication date 2005
Sub-type Article (original research)
DOI 10.1016/j.sigpro.2005.02.007
Open Access Status File (Author Post-print)
Volume 85
Issue 8
Start page 1546
End page 1556
Total pages 11
Place of publication Amsterdam
Publisher Elsevier Science Bv
Collection year 2005
Language eng
Subject C1
Abstract Recursive filters are widely used in image analysis due to their efficiency and simple implementation. However these filters have an initialisation problem which either produces unusable results near the image boundaries or requires costly approximate solutions such as extending the boundary manually. In this paper, we describe a method for the recursive filtering of symmetrically extended images for filters with symmetric denominator. We begin with an analysis of symmetric extensions and their effect on non-recursive filtering operators. Based on the non-recursive case, we derive a formulation of recursive filtering on symmetric domains as a linear but spatially varying implicit operator. We then give an efficient method for decomposing and solving the linear implicit system, along with a proof that this decomposition always exists. This decomposition needs to be performed only once for each dimension of the image. This yields a filtering which is both stable and consistent with the ideal infinite extension. The filter is efficient, requiring less computation than the standard recursive filtering. We give experimental evidence to verify these claims. (c) 2005 Elsevier B.V. All rights reserved.
Keyword Image Processing
Recursive Filtering
Symmetric Extension
Neumann Boundary Conditions
Computer Science, Hardware & Architecture
Engineering, Electrical & Electronic
Q-Index Code C1

Document type: Journal Article
Sub-type: Article (original research)
Collections: 2006 Higher Education Research Data Collection
School of Information Technology and Electrical Engineering Publications
 
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Citation counts: TR Web of Science Citation Count  Cited 4 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 4 times in Scopus Article | Citations
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Created: Wed, 15 Aug 2007, 06:05:41 EST