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

✍ Scribed by K. K. Shukla, Arvind K. Tiwari (auth.)

• Publisher: Springer-Verlag London
• Year: 2013
• Tongue: English
• Leaves: 96
• Series: SpringerBriefs in Computer Science
• Edition: 1
• Category: Library

✦ Synopsis

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.

✦ Table of Contents

Front Matter....Pages i-ix
Introduction....Pages 1-20
Filter Banks and DWT....Pages 21-36
Finite Precision Error Modeling and Analysis....Pages 37-49
PVM Implementation of DWT-Based Image Denoising....Pages 51-59
DWT-Based Power Quality Classification....Pages 61-81
Conclusions and Future Directions....Pages 83-84
Back Matter....Pages 85-91

✦ Subjects

Image Processing and Computer Vision; Signal, Image and Speech Processing; Algorithm Analysis and Problem Complexity