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Efficient Algorithms for Discrete Wavelet Transform

With Applications to Denoising and Fuzzy Inference Systems

  • Book
  • © 2013

Overview

Authors:
  1. K. K. Shukla

    1. Banaras Hindu University, Indian Institute of Technology, Varanasi, India

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  2. Arvind K. Tiwari

    1. GE India Technology Center, Bangalore, India

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  • Describes a mathematical model to predict the errors introduced in the implementation of the discrete wavelet transform (DWT) on fixed-point processors
  • Explores the application of DWT on benchmark signals and images in terms of denoising
  • Proposes a modified threshold selection and thresholding scheme
  • Includes supplementary material: sn.pub/extras

Part of the book series: SpringerBriefs in Computer Science (BRIEFSCOMPUTER)

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Table of contents (6 chapters)

  1. Front Matter

    Pages i-ix
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  2. Introduction

    • K. K. Shukla, Arvind K. Tiwari
    Pages 1-20

  3. Filter Banks and DWT

    • K. K. Shukla, Arvind K. Tiwari
    Pages 21-36

  4. Finite Precision Error Modeling and Analysis

    • K. K. Shukla, Arvind K. Tiwari
    Pages 37-49

  5. PVM Implementation of DWT-Based Image Denoising

    • K. K. Shukla, Arvind K. Tiwari
    Pages 51-59

  6. DWT-Based Power Quality Classification

    • K. K. Shukla, Arvind K. Tiwari
    Pages 61-81

  7. Conclusions and Future Directions

    • K. K. Shukla, Arvind K. Tiwari
    Pages 83-84

  8. Back Matter

    Pages 85-91
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Keywords

About this book

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.

Authors and Affiliations

  • Banaras Hindu University, Indian Institute of Technology, Varanasi, India

    K. K. Shukla

  • GE India Technology Center, Bangalore, India

    Arvind K. Tiwari

Bibliographic Information

  • Book Title: Efficient Algorithms for Discrete Wavelet Transform

  • Book Subtitle: With Applications to Denoising and Fuzzy Inference Systems

  • Authors: K. K. Shukla, Arvind K. Tiwari

  • Series Title: SpringerBriefs in Computer Science

  • DOI: https://doi.org/10.1007/978-1-4471-4941-5

  • Publisher: Springer London

  • eBook Packages: Computer Science, Computer Science (R0)

  • Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer-Verlag London Ltd., part of Springer Nature 2013

  • Softcover ISBN: 978-1-4471-4940-8Published: 07 February 2013

  • eBook ISBN: 978-1-4471-4941-5Published: 26 January 2013

  • Series ISSN: 2191-5768

  • Series E-ISSN: 2191-5776

  • Edition Number: 1

  • Number of Pages: IX, 91

  • Number of Illustrations: 15 b/w illustrations, 31 illustrations in colour

  • Topics: Image Processing and Computer Vision, Signal, Image and Speech Processing, Algorithm Analysis and Problem Complexity

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