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, J., Ledesma, S.
Formato: CONF
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: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 todo:paper_0277786X_v5622_nPART2_p617_Mazzaferri
record_format dspace
spelling todo:paper_0277786X_v5622_nPART2_p617_Mazzaferri2023-10-03T15:16:30Z Wavelet coefficients thresholding method applied to the correlation of noisy scenes Mazzaferri, J. Ledesma, S. 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. CONF info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/2.5/ar 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, J.
Ledesma, S.
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.
format CONF
author Mazzaferri, J.
Ledesma, S.
author_facet Mazzaferri, J.
Ledesma, S.
author_sort Mazzaferri, J.
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
url http://hdl.handle.net/20.500.12110/paper_0277786X_v5622_nPART2_p617_Mazzaferri
work_keys_str_mv AT mazzaferrij waveletcoefficientsthresholdingmethodappliedtothecorrelationofnoisyscenes
AT ledesmas waveletcoefficientsthresholdingmethodappliedtothecorrelationofnoisyscenes
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