A new approach for estimation of statistically matched wavelet

Gupta, Anubha; Joshi, Sachindra; Prasad, S.

doi:10.1109/tsp.2005.845470

Cited by 74 publications

(47 citation statements)

References 38 publications

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“…The resulting wavelets were acceptable according to constraints but both of these wavelet design methods were independent of the signals being analyzed. Gupta et al presented a method in the time domain based on maximizing the projection of the given signal into a successive scaling subspace and minimization in the wavelet subspace [8][9][10]. Tewfik et al presented a method which led to finding the best estimation of the desired signal with integer translates of a valid scaling function of finite fixed support , dilated by a given factor , at the proper scale [11].…”

Section: Improved Denoising Methods For Ultrasonic Echo With Mother Wamentioning

confidence: 99%

Improved Denoising Method for Ultrasonic Echo with Mother Wavelet Optimization and Best-Basis Selection

Mohammadi¹

2016

IJECE

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Weak features of ultrasonicnondestructive test signals are usually immersed in noisy signals. So, in this paper, we proposed an improved scheme for noise reduction and feature extraction based on discrete wavelet transform. The basis of the mother wavelet was selected to be matched to a given signal. Three different constraints were presented to minimize the error between the denoised and the given signal. It should be mentioned that such an optimum wavelet can represent the signal more compactly with a few large coefficients which can be considered as the signal features. Standard signals and simulated ultrasonic echo were used to evaluate the performance of the presented algorithms. Signal to error ratio was used to compare the designed wavelet performance with that of standard wavelets. Simulation results revealed that the proposed method outperformed the other presented methods and even standard wavelets. The results also has shown that the signal-based noise reduction algorithms make the feature extraction more reliable. Finally, the performance of the proposed algorithm was compared with other methods from different literatures. Keyword:Denoising Discrete wavelet transform Threshold Ultrasonic_NDT

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Section: Improved Denoising Methods For Ultrasonic Echo With Mother Wamentioning

confidence: 99%

Improved Denoising Method for Ultrasonic Echo with Mother Wavelet Optimization and Best-Basis Selection

Mohammadi¹

2016

IJECE

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“…Entre técnicas recientes de acoplamiento de wavelets más interesantes se encuentran propuestas por Chapa y Rao [6] A. Gupta et al [7]. En [6], los autores adaptan la wavelet generalizada de Meyer, minimizando la diferencia entre la wavelet y espectro de la señal, pero su método es complejo computacionalmente porque requiere una sincronización de fase, y es diseñado sólo para la detección de señales determinísticas.…”

Section: Introductionunclassified

“…En [6], los autores adaptan la wavelet generalizada de Meyer, minimizando la diferencia entre la wavelet y espectro de la señal, pero su método es complejo computacionalmente porque requiere una sincronización de fase, y es diseñado sólo para la detección de señales determinísticas. En [7], el método para estimación de filtros pasa altas de análisis wavelets se propone para señales que…”

Section: Introductionunclassified

Adjustment of Wavelet Filters for Image Compression Using Artificial Intelligence

et al. 2016

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Resumen-Se presenta un método para compresión de imágenes sin pérdidas, usando la transformada wavelet lifting con ajuste automático de coeficientes de filtros wavelets para una mayor compresión sin pérdidas. La propuesta se basa en reconocimiento de patrones utilizando clasificador 1-NN. Utilizando el reconocimiento de patrones se optimizan los coeficientes de los filtros lifting de manera global para cada imagen. La técnica propuesta fue aplicada para la compresión de imágenes de prueba y comparada con los filtros wavelets estándares CDF (2,2) y CDF (4,4), obteniendo resultados mejores en relación a la entropía obtenida para cada imágenes, así, como para el promedio general.Palabras clave-Compresión de imágenes sin pérdida, esquema lifting, wavelets, reconocimiento de patrones. Adjustment of Wavelet Filters for Image Compression Using Artificial IntelligenceAbstract-A method for lossless image compression using wavelet lifting transform with automatic adjustment of wavelet filter coefficients for better compression is presented. The proposal is based on pattern recognition by 1-NN classifier. Using the pattern recognition, the lifting filter coefficients are optimized globally for each image. The proposed technique was aplied to test images and the compression results were compared to the results produced by the standard CDF (2,2) and CDF (4,4) wavelet filters. The results obtained with the optimized wavelet filters are better in terms of the achieved entropy for all images, as well as for the overall performance.

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“…Among the recent methods of wavelet matching those proposed by Chapa and Rao [6] and A.Gupta et al [7] are the most notable. In [6], the authors adapt the generalized Meyer's wavelet minimizing the difference between wavelet and signal spectra, but their method is computationally expensive in phase matching and is designed for deterministic signal detection.…”

Section: Introductionmentioning

confidence: 99%

“…In [6], the authors adapt the generalized Meyer's wavelet minimizing the difference between wavelet and signal spectra, but their method is computationally expensive in phase matching and is designed for deterministic signal detection. In [7], the method of estimating of an analysis high pass wavelet filter is proposed for signals, which represent self-similar processes and their autocorrelation can be modeled analytically using the selfsimilarity index. Although authors state their method is simple, it requires in a selfsimilarity index a numerical search of a maximum likelihood estimator.…”

Section: Introductionmentioning

confidence: 99%

Wavelet Filter Adjusting for Image Lossless Compression Using Pattern Recognition

Pogrebnyak

Hernández-Bautista

Camacho-Nieto

et al. 2014

Lecture Notes in Computer Science

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Abstract.A method for image lossless compression using lifting scheme wavelet transform is presented. The proposed method adjusts wavelet filter coefficients analyzing signal spectral characteristics to obtain a higher compression ratio in comparison to the standard CDF(2,2) and CDF(4,4) filters. The proposal is based on spectral pattern recognition with 1-NN classifier. Spectral patterns of a small fixed length are formed for the entire image permitting thus the global optimization of the filter coefficients, equal for all decompositions. The proposed method was applied to a set of test images obtaining better results in entropy values in comparison to the standard wavelet lifting filters.

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A new approach for estimation of statistically matched wavelet

Cited by 74 publications

References 38 publications

Improved Denoising Method for Ultrasonic Echo with Mother Wavelet Optimization and Best-Basis Selection

Improved Denoising Method for Ultrasonic Echo with Mother Wavelet Optimization and Best-Basis Selection

Adjustment of Wavelet Filters for Image Compression Using Artificial Intelligence

Wavelet Filter Adjusting for Image Lossless Compression Using Pattern Recognition

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