Efficient Algorithms for Discrete Wavelet Transform: With Applications to Denoising and Fuzzy Inference...

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K K Shukla Arvind K. Tiwari

Efficient Algorithms for Discrete Wavelet Transform

Name: Efficient Algorithms for Discrete Wavelet Transform
Price: 101.37 AUD
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ISBN: 9781447149408

With Applications to Denoising and Fuzzy Inference Systems

K K Shukla, Arvind K. Tiwari

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English

Springer London Ltd
07 February 2013

Electronics engineering; Algorithms & data structures; Computer vision; Signal processing

Series: SpringerBriefs in Computer Science

Due to its inherent time-scale locality characteristics, the discrete wavelet transform (DWT) has received considerable attention in signal/image processing. Wavelet transforms have excellent energy compaction characteristics and can provide perfect reconstruction. The shifting (translation) and scaling (dilation) are unique to wavelets. Orthogonality of wavelets with respect to dilations leads to multigrid representation. As the computation of DWT involves filtering, an efficient filtering process is essential in DWT hardware implementation. In the multistage DWT, coefficients are calculated recursively, and in addition to the wavelet decomposition stage, extra space is required to store the intermediate coefficients. Hence, the overall performance depends significantly on the precision of the intermediate DWT coefficients. This work presents new implementation techniques of DWT, that are efficient in terms of computation, storage, and with better signal-to-noise ratio in the reconstructed signal.

By: K K Shukla, Arvind K. Tiwari
Imprint: Springer London Ltd
Country of Publication: United Kingdom
Edition: 2013 ed.
Dimensions: Height: 235mm, Width: 155mm, Spine: 8mm
Weight: 1.708kg
ISBN: 9781447149408
ISBN 10: 1447149408
Series: SpringerBriefs in Computer Science
Pages: 91
Publication Date: 07 February 2013
Audience: Professional and scholarly , Undergraduate
Format: Paperback
Publisher's Status: Active

Introduction.- Filter Banks and DWT.- Finite Precision Error Modeling and Analysis.- PVM Implementation of DWT-Based Image Denoising.- DWT-Based Power Quality Classification.- Conclusions and Future Directions.