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020 _a9781447149415
_9978-1-4471-4941-5
024 7 _a10.1007/978-1-4471-4941-5
_2doi
050 4 _aTA1637-1638
050 4 _aTA1634
072 7 _aUYT
_2bicssc
072 7 _aUYQV
_2bicssc
072 7 _aCOM012000
_2bisacsh
072 7 _aCOM016000
_2bisacsh
082 0 4 _a006.6
_223
082 0 4 _a006.37
_223
100 1 _aShukla, K. K.
_eauthor.
245 1 0 _aEfficient Algorithms for Discrete Wavelet Transform
_h[electronic resource] :
_bWith Applications to Denoising and Fuzzy Inference Systems /
_cby K. K. Shukla, Arvind K. Tiwari.
264 1 _aLondon :
_bSpringer London :
_bImprint: Springer,
_c2013.
300 _aIX, 91 p. 46 illus., 31 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSpringerBriefs in Computer Science,
_x2191-5768
505 0 _aIntroduction -- 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.
520 _aTransforms are an important part of an engineer’s toolkit for solving signal processing and polynomial computation problems. In contrast to the Fourier transform-based approaches where a fixed window is used uniformly for a range of frequencies, the wavelet transform uses short windows at high frequencies and long windows at low frequencies. This way, the characteristics of non-stationary disturbances can be more closely monitored. In other words, both time and frequency information can be obtained by wavelet transform. Instead of transforming a pure time description into a pure frequency description, the wavelet transform finds a good promise in a time-frequency description. Due to its inherent time-scale locality characteristics, the discrete wavelet transform (DWT) has received considerable attention in digital signal processing (speech and image processing), communication, computer science and mathematics. Wavelet transforms are known to have excellent energy compaction characteristics and are able to provide perfect reconstruction. Therefore, they are ideal for signal/image processing. The shifting (or translation) and scaling (or dilation) are unique to wavelets. Orthogonality of wavelets with respect to dilations leads to multigrid representation. The nature of wavelet computation forces us to carefully examine the implementation methodologies. 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 requirement, storage requirement, and with better signal-to-noise ratio in the reconstructed signal.
650 0 _aComputer science.
650 0 _aAlgorithms.
650 0 _aImage processing.
650 1 4 _aComputer Science.
650 2 4 _aImage Processing and Computer Vision.
650 2 4 _aSignal, Image and Speech Processing.
650 2 4 _aAlgorithm Analysis and Problem Complexity.
700 1 _aTiwari, Arvind K.
_eauthor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9781447149408
830 0 _aSpringerBriefs in Computer Science,
_x2191-5768
856 4 0 _uhttp://dx.doi.org/10.1007/978-1-4471-4941-5
912 _aZDB-2-SCS
942 _2Dewey Decimal Classification
_ceBooks
999 _c43701
_d43701