Many classic chirp signal processing algorithms may show significant performance degradation when the signal-to-noise ratio (SNR) is low. To address this problem, paper proposes a pre-filtering method in time-domain based on deep learning. Different from traditional filtering methods, proposed denoising convolutional neural network (DCNN) trained to recover pure noisy as much possible. Followin...

A new simple and efficient dual tree analytic wavelet transform based on Discrete Cosine Harmonic Wavelet Transform DCHWT (ADCHWT) has been proposed and is applied for signal and image denoising. The analytic DCHWT has been realized by applying DCHWT to the original signal and its Hilbert transform. The shift invariance and the envelope extraction properties of the ADCHWT have been found to be ...

Previous simulation experiments for the comparison of wavelet shrinkage denoising methods have used fixed signal classes defined by adding instances of noise to a single test signal. New simulation experiments are reported here with randomized signal classes defined by adding instances of noise to instances of randomized test signals. As expected, significantly greater variability in the perfor...

In all methods of image denoising there is a problem always exists that is how to distinguish noise and edge. Now wavelet and contourlet are main tools in image denoising, but threshold is the key in wavelet and contourlet denoising. In order to distinguish noise and edge well, most methods in wavelet denoising are about the improvement of threshold. Aiming to resolve this problem, a new method...

Array-based comparative genome hybridization (array CGH) is a recently developed high-throughput technique to detect DNA copy number aberrations. Typically, array CGH data is noisy. Wavelet denoising was previously shown to have superior performance for denoising array CGH data. However, the effect of different signal extensions methods on the performance of wavelet denoising in this particular...

Wavelet transforms enable us to represent signals with a high degree of sparsity. This is the principle behind a non-linear wavelet based signal estimation technique known as wavelet denoising. In this report we explore wavelet denoising of images using several thresholding techniques such as SUREShrink, VisuShrink and BayesShrink. Further, we use a Gaussian based model to perform combined deno...

Most of the wavelet threshold denoising methods need to calculate the corresponding threshold. The estimation of noise variance will directly affect the effect of threshold denoising method. This paper describes a new phase matching method of the noise variance estimation. The real-time noise can be estimated by this method. The experiments show that the method can greatly improve the signal to...

In this paper, we introduce a model-based Bayesian denoising framework for phonocardiogram (PCG) signals. The denoising framework is ounded on a new dynamical model for PCG, which is capable of generating realistic synthetic PCG signals. The introduced dynamical model is ased on PCG morphology and is inspired by electrocardiogram (ECG) dynamical model proposed by McSharry et al. and can represe...

A wavelet transform on the negative half real axis is developed using an average-interpolation scheme. This transform is redundant and can be used to perform causal wavelet processing, such as on-line signal denoising, without delay. Nonetheless, in practice some boundary effects occur and thus a small amount of delay is required to reduce them. The effect of this delay is studied using a numer...

This paper provides a compressive sensing (CS) method of denoising images using Bayesian framework. Some images, for example like magnetic resonance images (MRI) are usually very weak due to the presence of noise and due to the weak nature of the signal itself. So denoising boosts the true signal strength. Under Bayesian framework, we have used two different priors: sparsity and clusterdness in...