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Dual-Tree Complex Wavelets - their key properties and a range of image-processing applications

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  • UserProf Nick Kingsbury, Signal Processing and Communications Group, Department of Engineering, University of Cambridge
  • ClockWednesday 24 October 2012, 14:00-15:00
  • HouseLR4, Engineering, Department of.

If you have a question about this talk, please contact Rachel Fogg.

We will describe the Dual-Tree Complex Wavelet Transform (DT CWT ), a form of discrete wavelet transform which generates complex coefficients by using two trees of wavelet filters in parallel to obtain their real and imaginary parts. This introduces limited redundancy (2^m : 1 for m-dimensional signals) and the Hilbert-pair property is designed into the two wavelet bases such that the transform provides approximate shift invariance and directionally selective filters (properties lacking in the traditional wavelet transform), while preserving the usual properties of perfect reconstruction and computational efficiency with good well-balanced frequency responses. Energy is preserved in the Q-shift wavelet filters, resulting in tight-frame properties too. In the decade since the dual tree was first proposed, it has been applied to images and 3D datasets and has been shown to outperform traditional wavelets in a range of applications, which will be summarized. These will include image registration, fusion, sparsisty-based deconvolution, and object feature detectors and descriptors.

This talk is part of the Signal Processing and Communications Lab Seminars series.

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