Image reconstruction with two-dimensional piecewise polynomial convolution

Research output: Contribution to journalConference article

3 Citations (Scopus)

Abstract

This paper describes two-dimensional, non-separable, piecewise polynomial convolution for image reconstruction. We investigate a two-parameter kernel with support [-2,2]×[-2,2] and constrained for smooth reconstruction. Performance reconstructing a sampled random Markov field is superior to the traditional one-dimensional cubic convolution algorithm.

Original languageEnglish (US)
Pages (from-to)3237-3240
Number of pages4
JournalICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume6
StatePublished - Jan 1 1999
EventProceedings of the 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP-99) - Phoenix, AZ, USA
Duration: Mar 15 1999Mar 19 1999

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image reconstruction
Image reconstruction
Convolution
convolution integrals
polynomials
Polynomials

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

Cite this

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title = "Image reconstruction with two-dimensional piecewise polynomial convolution",
abstract = "This paper describes two-dimensional, non-separable, piecewise polynomial convolution for image reconstruction. We investigate a two-parameter kernel with support [-2,2]×[-2,2] and constrained for smooth reconstruction. Performance reconstructing a sampled random Markov field is superior to the traditional one-dimensional cubic convolution algorithm.",
author = "Reichenbach, {Stephen E} and Frank Geng",
year = "1999",
month = "1",
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language = "English (US)",
volume = "6",
pages = "3237--3240",
journal = "Proceedings - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing",
issn = "0736-7791",
publisher = "Institute of Electrical and Electronics Engineers Inc.",

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T1 - Image reconstruction with two-dimensional piecewise polynomial convolution

AU - Reichenbach, Stephen E

AU - Geng, Frank

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N2 - This paper describes two-dimensional, non-separable, piecewise polynomial convolution for image reconstruction. We investigate a two-parameter kernel with support [-2,2]×[-2,2] and constrained for smooth reconstruction. Performance reconstructing a sampled random Markov field is superior to the traditional one-dimensional cubic convolution algorithm.

AB - This paper describes two-dimensional, non-separable, piecewise polynomial convolution for image reconstruction. We investigate a two-parameter kernel with support [-2,2]×[-2,2] and constrained for smooth reconstruction. Performance reconstructing a sampled random Markov field is superior to the traditional one-dimensional cubic convolution algorithm.

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M3 - Conference article

VL - 6

SP - 3237

EP - 3240

JO - Proceedings - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing

JF - Proceedings - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing

SN - 0736-7791

ER -