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Introducing the locally stationary dual-tree complex wavelet model

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

Published
Publication date28/09/2016
Host publication2016 IEEE International Conference on Image Processing (ICIP)
PublisherIEEE
Pages3583-3587
Number of pages5
ISBN (electronic)9781467399616
ISBN (print)9781467399623
<mark>Original language</mark>English
Event2016 IEEE International Conference on Image Processing (ICIP) - Phoenix, United States
Duration: 25/09/2016 → 28/09/2016
https://ieeexplore.ieee.org/xpl/conhome/7527113/proceeding

Conference

Conference2016 IEEE International Conference on Image Processing (ICIP)
Country/TerritoryUnited States
CityPhoenix
Period25/09/16 → 28/09/16
Internet address

Conference

Conference2016 IEEE International Conference on Image Processing (ICIP)
Country/TerritoryUnited States
CityPhoenix
Period25/09/16 → 28/09/16
Internet address

Abstract

This paper reconciles Kingsbury's dual-tree complex wavelets with Nason and Eckley's locally stationary model. We here establish that the dual-tree wavelets admit an invertible de-biasing matrix and that this matrix can be used to invert the covariance relation. We also show that the added directional selectivity of the proposed model adds utility to the standard two-dimensional local stationary model. Non-stationarity detection on random fields is used as a motivating example. Experiments confirm that the dual-tree model can distinguish anisotropic non-stationarities significantly better than the current model.

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©2016 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.