Topic
Harmonic wavelet transform
About: Harmonic wavelet transform is a research topic. Over the lifetime, 9602 publications have been published within this topic receiving 247336 citations.
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TL;DR: An image denoising procedure based on a 2D scale-mixing complex-valued wavelet transform that exhibits excellent quantitative and visual performance is introduced and demonstrated by simulation on standard test images.
Abstract: This paper introduces an image denoising procedure based on a 2D scale-mixing complex-valued wavelet transform. Both the minimal (unitary) and redundant (maximum overlap) versions of the transform are used. The covariance structure of white noise in wavelet domain is established. Estimation is performed via empirical Bayesian techniques, including versions that preserve the phase of the complex-valued wavelet coefficients and those that do not. The new procedure exhibits excellent quantitative and visual performance, which is demonstrated by simulation on standard test images.
40 citations
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TL;DR: In this article, fast Fourier transform and wavelet analysis methods were used to denoise electrical signals by a soft thresholding technique, where the signal can be divided into three different regions each region has different thresholds; therefore, different frequency components can be used independently in each region.
Abstract: Fast Fourier transform and wavelet analysis methods were used to denoise electrical signals by a soft thresholding technique A neutron pulse of 14 MeV is sent over a sample; as a consequence of the interaction between the sample and the neutrons, multi-spectral gamma rays are emitted by the sample The gamma-rays were measured using three photo multiplier tubes, which detect optical signals coming from three filtered detectors made of plastic scintillator material Applying wavelet analysis was possible to realize that the signal can be divided into three different regions Each region has different thresholds; therefore, different frequency components can be used independently in each region Comparisons of this method with the fast Fourier transform are presented In this particular application, it was found that the wavelet transform produces a much better way of denoising the signals in terms of keeping the characteristic high frequency at the start of the signals; this feature allows the differential classification of the signals and the consequent identification of the component of the sample The preliminary results presented here are the first attempt to identify the chemical composition of samples using this method
40 citations
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TL;DR: In this paper, a method that combines a wavelet transform and hyperbolic velocity filtering is presented for different types of noise attenuation, such as ground roll and CMP fold areas.
Abstract: The wavelet domain is convenient for various seismic data processing tasks, such as noise attenuation, because data are represented in time and frequency simultaneously. A method that combines a wavelet transform and localized semblanceweighted hyperbolic velocity filtering is presented for different types of noise attenuation. One benefit of applying noise attenuation in the wavelet domain is that some spectral components of signal are preserved untouched, and only the part of the signal whose frequency band overlaps with noise is filtered. A test example with symmetric-sampled 3D cross spread data shows that after wavelet transform ground roll noise attenuation, the data quality is largely improved, particularly for low CMP fold areas and deep portions of the data which are contaminated by noise.
40 citations
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23 Mar 1992TL;DR: The authors have developed an expansion they call the chirplet transform, which has been successfully applied to a wide variety of signal processing applications, including radar and image processing.
Abstract: The authors have developed an expansion they call the chirplet transform. It has been successfully applied to a wide variety of signal processing applications, including radar and image processing. There has been a recent debate as to the relative merits of an affine-in-time (wavelet) transform and the classical short-time Fourier transform (STFT) for the analysis of nonstationary phenomena. Chirplet filters embody both the wavelet and STFT as special cases by decoupling the filter bandwidths and center frequencies. Chirplets, by their embodiment of affine geometry in the time-frequency (TF) plane, may also include shears in time and frequency (chirps) and even time-bandwidth product variation (noise bursts) if desired. The most general chirplets may be derived from one or more basic ('mother') chirplets by the transformations or perspective geometry in the TF plane. >
40 citations