Image processing through multiscale analysis and measurement noise modeling

F. Murtagh, J. L. Starck

Research output: Contribution to journalArticle

22 Citations (Scopus)

Abstract

We describe a range of powerful multiscale analysis methods. We also focus on the pivotal issue of measurement noise in the physical sciences. From multiscale analysis and noise modeling, we develop a comprehensive methodology for data analysis of 2D images, 1D signals (or spectra), and point pattern data. Noise modeling is based on the following: (i) multiscale transforms, including wavelet transforms; (ii) a data structure termed the multiresolution support; and (iii) multiple scale significance testing. The latter two aspects serve to characterize signal with respect to noise. The data analysis objectives we deal with include noise filtering and scale decomposition for visualization or feature detection.

LanguageEnglish
Pages95-103
Number of pages9
JournalStatistics and Computing
Volume10
Issue number2
DOIs
Publication statusPublished - 1 Apr 2000
Externally publishedYes

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Multiscale Analysis
Image Processing
Data analysis
Image processing
Noise Filtering
Feature Detection
Multiple Scales
Multiresolution
Modeling
Wavelet transforms
Wavelet Transform
Data structures
Data Structures
Visualization
Transform
Decomposition
Decompose
Testing
Methodology
Range of data

Cite this

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Image processing through multiscale analysis and measurement noise modeling. / Murtagh, F.; Starck, J. L.

In: Statistics and Computing, Vol. 10, No. 2, 01.04.2000, p. 95-103.

Research output: Contribution to journalArticle

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