The Experts below are selected from a list of 6912 Experts worldwide ranked by ideXlab platform

G.b. Benie - One of the best experts on this subject based on the ideXlab platform.

  • Analysis of speckle Noise Contribution on wavelet decomposition of SAR images
    IEEE Transactions on Geoscience and Remote Sensing, 1998
    Co-Authors: M. Simard, G. Degrandi, K.p.b. Thomson, G.b. Benie
    Abstract:

    This paper describes the use of the wavelet transform for multiscale texture analysis. One of the basic problems is that texture measures have to adapt to the peculiarity of radar images that contain multiplicative speckle Noise. In this paper, the focus is on the effect of speckle on the wavelet transform. The effect is first assessed analytically. It is shown that the wavelet coefficients are modulated by the multiplicative character of the speckle in a manner that is proportional to the target mean backscattering coefficient. The effect of speckle correlation is also demonstrated. Wavelet decomposition is then applied to a simulated radar image generated by a Monte Carlo approach and based on a statistical model. Modeling shows that the correlation properties of speckle have an effect up to a scale that corresponds to its granular size. The results also show that the main Contribution to the wavelet transform for an homogeneous area is the first-order statistical distribution of speckle, which remains important even at large scales. The results are then compared to a ERS-1 synthetic aperture radar (SAR) image of a primary tropical forest region.

P. Duvaut - One of the best experts on this subject based on the ideXlab platform.

  • Bernoulli-Gaussian deconvolution in non-Gaussian Noise, Contribution of wavelet decomposition
    1996 8th European Signal Processing Conference (EUSIPCO 1996), 1996
    Co-Authors: H. Rousseau, P. Duvaut
    Abstract:

    We introduce a method to restore Bernoulli-Gaussian processes immerged in a non-gaussian Noise. It uses wavelet decomposition to "gaussianize" the Noise. The convergence, after wavelet projection, of some non-gaussian Noise to a gaussian Noise quantifies the quality of the "gaussianization" effect of the wavelet. This property is used to apply a Bernoulli-Gaussian algorithm at each scale of wavelet decomposition. After, we use a fusion strategy to merge all results. We obtain also a new deconvolution algorithm which is very performant, for all satistical Noises, when the Noise variance is not well estimated. When the Noise variance is correctly estimated, it improves the classical Bernoulli-Gaussian algorithm for strongly non-Gaussian Noises.

Teng-kuei Su - One of the best experts on this subject based on the ideXlab platform.

  • SESSION TITLE: Drive-By-Noise Study of Tire Noise for Vehicle Noise Control
    2020
    Co-Authors: Yu-ying Chen, Teng-kuei Su
    Abstract:

    Most popular method to control vehicle Noise is to limit acceleration Noise. Perform acceleration, cruise and coast test, to evaluate the tire Noise Contribution to acceleration and cruise Noise. For car, tire Noise is 20 ~ 50 % Contribution to acceleration. However, for large vehicle, the tire Noise Contribution is lower. Nevertheless, tire Noise Contribution reach 80 ~ 100 % in cruise condition at 80 and 100 km/h. Base on 109 samples, tire Noise is positive correlation to tire width. During vehicle type approval, wider tire of all options for vehicle should be used. It is possible to predict on road Noise from in lab test

  • Study of Tire Noise for Vehicle Noise Control
    SAE Technical Paper Series, 2005
    Co-Authors: Jeff S. C. Chuang, Yu-ying Chen, Teng-kuei Su
    Abstract:

    Most popular method to control vehicle Noise is to limit acceleration Noise. Perform acceleration, cruise and coast test, to evaluate the tire Noise Contribution to acceleration and cruise Noise. For car, tire Noise is 20 ~ 50 % Contribution to acceleration. However, for large vehicle, the tire Noise Contribution is lower. Nevertheless, tire Noise Contribution reach 80 ~ 100 % in cruise condition at 80 and 100 km/h. Base on 109 samples, tire Noise is positive correlation to tire width. During vehicle type approval, wider tire of all options for vehicle should be used. It is possible to predict on road Noise from in lab test

M. Simard - One of the best experts on this subject based on the ideXlab platform.

  • Analysis of speckle Noise Contribution on wavelet decomposition of SAR images
    IEEE Transactions on Geoscience and Remote Sensing, 1998
    Co-Authors: M. Simard, G. Degrandi, K.p.b. Thomson, G.b. Benie
    Abstract:

    This paper describes the use of the wavelet transform for multiscale texture analysis. One of the basic problems is that texture measures have to adapt to the peculiarity of radar images that contain multiplicative speckle Noise. In this paper, the focus is on the effect of speckle on the wavelet transform. The effect is first assessed analytically. It is shown that the wavelet coefficients are modulated by the multiplicative character of the speckle in a manner that is proportional to the target mean backscattering coefficient. The effect of speckle correlation is also demonstrated. Wavelet decomposition is then applied to a simulated radar image generated by a Monte Carlo approach and based on a statistical model. Modeling shows that the correlation properties of speckle have an effect up to a scale that corresponds to its granular size. The results also show that the main Contribution to the wavelet transform for an homogeneous area is the first-order statistical distribution of speckle, which remains important even at large scales. The results are then compared to a ERS-1 synthetic aperture radar (SAR) image of a primary tropical forest region.

H. Rousseau - One of the best experts on this subject based on the ideXlab platform.

  • Bernoulli-Gaussian deconvolution in non-Gaussian Noise, Contribution of wavelet decomposition
    1996 8th European Signal Processing Conference (EUSIPCO 1996), 1996
    Co-Authors: H. Rousseau, P. Duvaut
    Abstract:

    We introduce a method to restore Bernoulli-Gaussian processes immerged in a non-gaussian Noise. It uses wavelet decomposition to "gaussianize" the Noise. The convergence, after wavelet projection, of some non-gaussian Noise to a gaussian Noise quantifies the quality of the "gaussianization" effect of the wavelet. This property is used to apply a Bernoulli-Gaussian algorithm at each scale of wavelet decomposition. After, we use a fusion strategy to merge all results. We obtain also a new deconvolution algorithm which is very performant, for all satistical Noises, when the Noise variance is not well estimated. When the Noise variance is correctly estimated, it improves the classical Bernoulli-Gaussian algorithm for strongly non-Gaussian Noises.