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Efficient algorithms for discrete wavelet transform: with applications to denoising and fuzzy inference systems
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 sca...
| Autores principales: | , |
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| Lenguaje: | eng |
| Publicado: |
Springer
2013
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| Materias: | |
| Acceso en línea: | http://cds.cern.ch/record/1618420 |
| _version_ | 1780932925390323712 |
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| author | Shukla, K K Tiwari, Arvind K |
| author_facet | Shukla, K K Tiwari, Arvind K |
| author_sort | Shukla, K K |
| collection | CERN |
| description | 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 |
| id | cern-1618420 |
| institution | Organización Europea para la Investigación Nuclear |
| language | eng |
| publishDate | 2013 |
| publisher | Springer |
| record_format | invenio |
| spelling | cern-16184202021-04-21T21:58:10Zhttp://cds.cern.ch/record/1618420engShukla, K KTiwari, Arvind KEfficient algorithms for discrete wavelet transform: with applications to denoising and fuzzy inference systemsMathematical Physics and Mathematics 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 Springeroai:cds.cern.ch:16184202013 |
| spellingShingle | Mathematical Physics and Mathematics Shukla, K K Tiwari, Arvind K Efficient algorithms for discrete wavelet transform: with applications to denoising and fuzzy inference systems |
| title | Efficient algorithms for discrete wavelet transform: with applications to denoising and fuzzy inference systems |
| title_full | Efficient algorithms for discrete wavelet transform: with applications to denoising and fuzzy inference systems |
| title_fullStr | Efficient algorithms for discrete wavelet transform: with applications to denoising and fuzzy inference systems |
| title_full_unstemmed | Efficient algorithms for discrete wavelet transform: with applications to denoising and fuzzy inference systems |
| title_short | Efficient algorithms for discrete wavelet transform: with applications to denoising and fuzzy inference systems |
| title_sort | efficient algorithms for discrete wavelet transform: with applications to denoising and fuzzy inference systems |
| topic | Mathematical Physics and Mathematics |
| url | http://cds.cern.ch/record/1618420 |
| work_keys_str_mv | AT shuklakk efficientalgorithmsfordiscretewavelettransformwithapplicationstodenoisingandfuzzyinferencesystems AT tiwariarvindk efficientalgorithmsfordiscretewavelettransformwithapplicationstodenoisingandfuzzyinferencesystems |