Papers by Keyword: DT-CWT

Abstract: A method based on Dual-Tree Complex Wavelet Transform (DT-CWT) was proposed for enhancing the images. By taking into account the non-Gaussian probability distribution and the statistical dependencies among wavelet coefficients of some signals, and by taking advantage of near shift-invariance of DT-CWT, it can obtain higher signal-to-noise ratio (SNR) than common wavelet denoising methods. The simulation results show that the proposed method is better than the traditional methods. It has a good enhancement performance which can improve the details of the image automatically.

Abstract: There are strong dependencies between wavelet coefficients of speech signal,in this article,based on that,a new corresponding nonlinear threshold function derived in Bayesian framework is proposed to decrease the effect of the ambient noise.Analysis of the data shows the effectiveness of the proposed method that it removes white noise more effectually and gets better edge preservation.

Abstract: In this paper, we propose a new improved MC-CDMA system which combines spreading codes from Quasi-Orthogonal Matrix with Orthogonal Complex Wavelet Division Multiplexing (OCWDM). The system is implemented by complex wavelet filters which are able to lower computational complexity and increase flexibility. Novel spreading codes from Quasi-Orthogonal Matrix can be expanded randomly. It can increase user number hugely in system. Improved MC-CDMA named OCWDM-CDMA has much higher frequency spectrum efficiency and high data rate than conventional MC-CDMA. The system also has better BER performance in Gaussian channel than conventional system for much more users.

Abstract: A matching pursuit method based on Dual-Tree Complex Wavelet Transform (DT-CWT) is proposed for extracting feature. Many new orthogonal wavelet bases formed Hilbert transform pairs is constructed by the method which is based on the sufficient and necessary condition on constructing wavelet, via the flat delay filter, and translated the problem into resolving algebraic equations. And taking these wavelets as choice object, a matching pursuit method based on DT-CWT is used for extracting feature. The matching pursuit method is based on series expansion of the signal by a set of elementary functions of orthogonal wavelets formed Hilbert transform pairs to match feature more effectively. Simulation testing and field experiments confirm that the proposed method is effective especially in extracting impulsive feature on high intensity noise, which matching pursuit method based on Discrete Wavelet Transform and other wavelet de-noising methods based on threshold and frequency-band, etc cannot do it completely.

Abstract: A new signal-denoising approach based on DT-CWT (Dual-Tree Complex Wavelet Transform) is presented in this paper to extract feature information from microstructure profile. It takes advantage of shift invariance of DT-CWT, non-Gaussian probability distribution for the wavelet coefficients and the statistical dependencies between a coefficient and its parent. This approach substantially improved the performance of classical wavelet denoising algorithms, both in terms of SNR and in terms of visual artifacts. A simulated MEMS microstructure signal is analyzed.