Wavelet coefficients thresholding method applied to the correlation of noisy scenes

The distortion of a signal due to noise contamination can be overcome by using a decomposition of the signal in a base of wavelets. If the decomposition coefficients are small compared with the noise, the scene is dominated by the distortion. On the contrary, if they are bigger in absolute value, th...

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Detalles Bibliográficos
Autores principales:	Mazzaferri, Javier Esteban, Ledesma, Silvia Adriana
Publicado:	2004
Materias:	Nonlinear optical signal processing Optical correlators Optical imaging processing Pattern recognition and feature extraction Optical imaginng processing Distortion (waves) Feature extraction Functions Image analysis Mathematical models Nonlinear optics Optical correlation Signal processing Signal to noise ratio Spurious signal noise Threshold elements Wavelet transforms Pattern recognition
Acceso en línea:	https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_0277786X_v5622_nPART2_p617_Mazzaferri http://hdl.handle.net/20.500.12110/paper_0277786X_v5622_nPART2_p617_Mazzaferri
Aporte de:	Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) de Universidad de Buenos Aires

id	paper:paper_0277786X_v5622_nPART2_p617_Mazzaferri
record_format	dspace
spelling	paper:paper_0277786X_v5622_nPART2_p617_Mazzaferri2023-06-08T15:26:22Z Wavelet coefficients thresholding method applied to the correlation of noisy scenes Mazzaferri, Javier Esteban Ledesma, Silvia Adriana Nonlinear optical signal processing Optical correlators Optical imaging processing Pattern recognition and feature extraction Nonlinear optical signal processing Optical correlators Optical imaginng processing Pattern recognition and feature extraction Distortion (waves) Feature extraction Functions Image analysis Mathematical models Nonlinear optics Optical correlation Signal processing Signal to noise ratio Spurious signal noise Threshold elements Wavelet transforms Pattern recognition The distortion of a signal due to noise contamination can be overcome by using a decomposition of the signal in a base of wavelets. If the decomposition coefficients are small compared with the noise, the scene is dominated by the distortion. On the contrary, if they are bigger in absolute value, the signal is stronger that the noise. A way of reconstructing an image with a lower level of noise is accomplished neglecting the coefficients which values are lower than a threshold, and replacing them by zero. In this work we present a method that applies the thresholding of the wavelet coefficients in order to perform pattern recognition of noisy scenes. The method could be implemented in optical processing by using a Vander Lugt correlator architecture operating with liquid crystal displays. The function to be recognized is decomposed in sub-bands based on the Gabor decomposition, in the frequency plane. Hard thresholding is performed and the threshold is generated with accurate support functions in the filter plane. The criterion for the threshold selection is chosen to optimize the signal to noise ratio in the output plane. Numerical simulations results are shown and comparisons with other filters are made. Fil:Mazzaferri, J. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. Fil:Ledesma, S. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. 2004 https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_0277786X_v5622_nPART2_p617_Mazzaferri http://hdl.handle.net/20.500.12110/paper_0277786X_v5622_nPART2_p617_Mazzaferri
institution	Universidad de Buenos Aires
institution_str	I-28
repository_str	R-134
collection	Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA)
topic	Nonlinear optical signal processing Optical correlators Optical imaging processing Pattern recognition and feature extraction Nonlinear optical signal processing Optical correlators Optical imaginng processing Pattern recognition and feature extraction Distortion (waves) Feature extraction Functions Image analysis Mathematical models Nonlinear optics Optical correlation Signal processing Signal to noise ratio Spurious signal noise Threshold elements Wavelet transforms Pattern recognition
spellingShingle	Nonlinear optical signal processing Optical correlators Optical imaging processing Pattern recognition and feature extraction Nonlinear optical signal processing Optical correlators Optical imaginng processing Pattern recognition and feature extraction Distortion (waves) Feature extraction Functions Image analysis Mathematical models Nonlinear optics Optical correlation Signal processing Signal to noise ratio Spurious signal noise Threshold elements Wavelet transforms Pattern recognition Mazzaferri, Javier Esteban Ledesma, Silvia Adriana Wavelet coefficients thresholding method applied to the correlation of noisy scenes
topic_facet	Nonlinear optical signal processing Optical correlators Optical imaging processing Pattern recognition and feature extraction Nonlinear optical signal processing Optical correlators Optical imaginng processing Pattern recognition and feature extraction Distortion (waves) Feature extraction Functions Image analysis Mathematical models Nonlinear optics Optical correlation Signal processing Signal to noise ratio Spurious signal noise Threshold elements Wavelet transforms Pattern recognition
description	The distortion of a signal due to noise contamination can be overcome by using a decomposition of the signal in a base of wavelets. If the decomposition coefficients are small compared with the noise, the scene is dominated by the distortion. On the contrary, if they are bigger in absolute value, the signal is stronger that the noise. A way of reconstructing an image with a lower level of noise is accomplished neglecting the coefficients which values are lower than a threshold, and replacing them by zero. In this work we present a method that applies the thresholding of the wavelet coefficients in order to perform pattern recognition of noisy scenes. The method could be implemented in optical processing by using a Vander Lugt correlator architecture operating with liquid crystal displays. The function to be recognized is decomposed in sub-bands based on the Gabor decomposition, in the frequency plane. Hard thresholding is performed and the threshold is generated with accurate support functions in the filter plane. The criterion for the threshold selection is chosen to optimize the signal to noise ratio in the output plane. Numerical simulations results are shown and comparisons with other filters are made.
author	Mazzaferri, Javier Esteban Ledesma, Silvia Adriana
author_facet	Mazzaferri, Javier Esteban Ledesma, Silvia Adriana
author_sort	Mazzaferri, Javier Esteban
title	Wavelet coefficients thresholding method applied to the correlation of noisy scenes
title_short	Wavelet coefficients thresholding method applied to the correlation of noisy scenes
title_full	Wavelet coefficients thresholding method applied to the correlation of noisy scenes
title_fullStr	Wavelet coefficients thresholding method applied to the correlation of noisy scenes
title_full_unstemmed	Wavelet coefficients thresholding method applied to the correlation of noisy scenes
title_sort	wavelet coefficients thresholding method applied to the correlation of noisy scenes
publishDate	2004
url	https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_0277786X_v5622_nPART2_p617_Mazzaferri http://hdl.handle.net/20.500.12110/paper_0277786X_v5622_nPART2_p617_Mazzaferri
work_keys_str_mv	AT mazzaferrijavieresteban waveletcoefficientsthresholdingmethodappliedtothecorrelationofnoisyscenes AT ledesmasilviaadriana waveletcoefficientsthresholdingmethodappliedtothecorrelationofnoisyscenes
_version_	1768545141387165696

Wavelet coefficients thresholding method applied to the correlation of noisy scenes

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