Sequential Bayesian wavelet denoising
We propose a wavelet model that incorporates coefficient correlation and is expressed in state-space form, allowing the development and application of sequential estimation algorithms for wavelet denoising. We detail a sequential simulation-based estimation algorithm based on particle filters. This...
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| Format: | Conference item |
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IEEE
1999
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| _version_ | 1826267409966694400 |
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| author | Coates, M Doucet, A |
| author_facet | Coates, M Doucet, A |
| author_sort | Coates, M |
| collection | OXFORD |
| description | We propose a wavelet model that incorporates coefficient correlation and is expressed in state-space form, allowing the development and application of sequential estimation algorithms for wavelet denoising. We detail a sequential simulation-based estimation algorithm based on particle filters. This algorithm allows Bayesian wavelet denoising to be performed on-line, enabling it to process a vast dataset, and it is intrinsically parallelizable. The experiments indicate that the algorithm performance is comparable to the majority of Bayesian framework batch-based algorithms. © 1999 IEEE. |
| first_indexed | 2024-03-06T20:53:46Z |
| format | Conference item |
| id | oxford-uuid:387d4484-b08c-4ed0-a164-a98198819cd7 |
| institution | University of Oxford |
| last_indexed | 2024-03-06T20:53:46Z |
| publishDate | 1999 |
| publisher | IEEE |
| record_format | dspace |
| spelling | oxford-uuid:387d4484-b08c-4ed0-a164-a98198819cd72022-03-26T13:50:20ZSequential Bayesian wavelet denoisingConference itemhttp://purl.org/coar/resource_type/c_5794uuid:387d4484-b08c-4ed0-a164-a98198819cd7Symplectic Elements at OxfordIEEE1999Coates, MDoucet, AWe propose a wavelet model that incorporates coefficient correlation and is expressed in state-space form, allowing the development and application of sequential estimation algorithms for wavelet denoising. We detail a sequential simulation-based estimation algorithm based on particle filters. This algorithm allows Bayesian wavelet denoising to be performed on-line, enabling it to process a vast dataset, and it is intrinsically parallelizable. The experiments indicate that the algorithm performance is comparable to the majority of Bayesian framework batch-based algorithms. © 1999 IEEE. |
| spellingShingle | Coates, M Doucet, A Sequential Bayesian wavelet denoising |
| title | Sequential Bayesian wavelet denoising |
| title_full | Sequential Bayesian wavelet denoising |
| title_fullStr | Sequential Bayesian wavelet denoising |
| title_full_unstemmed | Sequential Bayesian wavelet denoising |
| title_short | Sequential Bayesian wavelet denoising |
| title_sort | sequential bayesian wavelet denoising |
| work_keys_str_mv | AT coatesm sequentialbayesianwaveletdenoising AT douceta sequentialbayesianwaveletdenoising |