Interference Image Edge Detection Based on Wavelet Packet for Laser Interferometer Hydrophone

Article Preview

Abstract:

An interference image edge detection method for laser interferometer hydrophone based on wavelet packet is investigated. Considering the effect of ocean background noise on the interference image, we propose the use of a class of wavelet packet functions to make decomposition on noisy interference image, and then make reconstruction on approximate parts of the decomposed image. The approximate parts with the wavelet packet decomposition is of more clear image layers comparison to the original image, and the wavelet packet decomposition method can reduce the influence of background noise on the interference image. Taking the wavelet packet decomposition method and other edge detection algorithms for contrast, it is proved that the wavelet packet decomposition method for edge detection algorithm is more effective than the other algorithms.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

670-673

Citation:

Online since:

December 2013

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2014 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

[1] Mallat S G, A Theory of Multiresolution Signal Decomposition: The Wavelet Representation, IEEE Transactions on Pattern Analysis and Machine Intelligence, 11: 674~693(1989).

DOI: 10.1109/34.192463

Google Scholar

[2] Mallat S, Zhong S, Characterization of Signal from Multi-scale Edges. IEEE Trans. On Pattern Analysis and Machine Intelligence, 14(7): 710~732(1992).

DOI: 10.1109/34.142909

Google Scholar

[3] Serroukh A, Walden A T, and Percival D B, Statistical Properties and Uses of the Wavelet Variance Estimator for the Scale Analysis of Time Series, Journal of the American Statistical Association, 95: 184~196(2000).

DOI: 10.1080/01621459.2000.10473913

Google Scholar

[4] Whitcher B, Guttorp P and Percival D B, Multiscale Detection and Location of Multiple Variance Changes in the Presence of Long Memory, Journal of Statistical Computation and Simulation, 68: 65~88 (2000).

DOI: 10.1080/00949650008812056

Google Scholar

[5] Percival D B and Walden A T. Wavelet Methods for Time Series Analysis, Cambridge: Cambridge University Press.

Google Scholar

[6] Walden A T, Contreras Cristan A, The Phase-Corrected Undedicated Discrete Wavelet Packet Transform and its Application to Interpreting the Timing of Events, Proceedings of the Royal Society of London, A: 2243~2266(1998).

DOI: 10.1098/rspa.1998.0257

Google Scholar