Denoising and compression in wavelet domain via projection onto approximation coefficients

We describe a new filtering approach in the wavelet domain for image denoising and compression, based on the projections of details subbands coefficients (resultants of the splitting procedure, typical in wavelet domain) onto the approximation subband coefficients (much less noisy). The new algorith...

Descripción completa

Detalles Bibliográficos
Autor principal: Mastriani, Mario
Publicado: 2009
Materias:
Compression
Denoising
Projections
Wavelets
Approximation coefficients
Compression rates
De-noising
Subband coefficients
Subbands
Wavelet domain
Compaction
Engineering
Technology
Approximation algorithms
Acceso en línea:https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_2010376X_v35_n_p622_Mastriani
http://hdl.handle.net/20.500.12110/paper_2010376X_v35_n_p622_Mastriani
Aporte de:
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) de Universidad de Buenos Aires
id paper:paper_2010376X_v35_n_p622_Mastriani
record_format dspace
spelling paper:paper_2010376X_v35_n_p622_Mastriani2023-06-08T16:32:57Z Denoising and compression in wavelet domain via projection onto approximation coefficients Mastriani, Mario Compression Denoising Projections Wavelets Approximation coefficients Compression rates De-noising Projections Subband coefficients Subbands Wavelet domain Wavelets Compaction Engineering Technology Approximation algorithms We describe a new filtering approach in the wavelet domain for image denoising and compression, based on the projections of details subbands coefficients (resultants of the splitting procedure, typical in wavelet domain) onto the approximation subband coefficients (much less noisy). The new algorithm is called Projection Onto Approximation Coefficients (POAC). As a result of this approach, only the approximation subband coefficients and three scalars are stored and/or transmitted to the channel. Besides, with the elimination of the details subbands coefficients, we obtain a bigger compression rate. Experimental results demonstrate that our approach compares favorably to more typical methods of denoising and compression in wavelet domain. Fil:Mastriani, M. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. 2009 https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_2010376X_v35_n_p622_Mastriani http://hdl.handle.net/20.500.12110/paper_2010376X_v35_n_p622_Mastriani
institution Universidad de Buenos Aires
institution_str I-28
repository_str R-134
collection Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA)
topic Compression
Denoising
Projections
Wavelets
Approximation coefficients
Compression rates
De-noising
Projections
Subband coefficients
Subbands
Wavelet domain
Wavelets
Compaction
Engineering
Technology
Approximation algorithms
spellingShingle Compression
Denoising
Projections
Wavelets
Approximation coefficients
Compression rates
De-noising
Projections
Subband coefficients
Subbands
Wavelet domain
Wavelets
Compaction
Engineering
Technology
Approximation algorithms
Mastriani, Mario
Denoising and compression in wavelet domain via projection onto approximation coefficients
topic_facet Compression
Denoising
Projections
Wavelets
Approximation coefficients
Compression rates
De-noising
Projections
Subband coefficients
Subbands
Wavelet domain
Wavelets
Compaction
Engineering
Technology
Approximation algorithms
description We describe a new filtering approach in the wavelet domain for image denoising and compression, based on the projections of details subbands coefficients (resultants of the splitting procedure, typical in wavelet domain) onto the approximation subband coefficients (much less noisy). The new algorithm is called Projection Onto Approximation Coefficients (POAC). As a result of this approach, only the approximation subband coefficients and three scalars are stored and/or transmitted to the channel. Besides, with the elimination of the details subbands coefficients, we obtain a bigger compression rate. Experimental results demonstrate that our approach compares favorably to more typical methods of denoising and compression in wavelet domain.
author Mastriani, Mario
author_facet Mastriani, Mario
author_sort Mastriani, Mario
title Denoising and compression in wavelet domain via projection onto approximation coefficients
title_short Denoising and compression in wavelet domain via projection onto approximation coefficients
title_full Denoising and compression in wavelet domain via projection onto approximation coefficients
title_fullStr Denoising and compression in wavelet domain via projection onto approximation coefficients
title_full_unstemmed Denoising and compression in wavelet domain via projection onto approximation coefficients
title_sort denoising and compression in wavelet domain via projection onto approximation coefficients
publishDate 2009
url https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_2010376X_v35_n_p622_Mastriani
http://hdl.handle.net/20.500.12110/paper_2010376X_v35_n_p622_Mastriani
work_keys_str_mv AT mastrianimario denoisingandcompressioninwaveletdomainviaprojectionontoapproximationcoefficients
_version_ 1768546696323661824

Ejemplares similares