This communication aims to combine several previously proposed wavelet denoising algorithms into a novel heuristic block method. The proposed “hysteresis” thresholding uses two thresholds simultaneously in order to combine detection and minimal alteration of informative features of the processed signal. This approach exploits the graph structure of the wavelet decomposition to detect clusters o...

A denoising unit based on wavelet multiresolution analysis is added ahead of the multilayered perceptron with global recurrent connections. The learning algorithm is developed which uses the same cost function for setting all free parameters, those of the denoising unit and those of the neural network. It is illustrated that the proposed model outmatches the models without denoising unit and/or...

Denoising source separation is a recently introduced framework for building source separation algorithms around denoising procedures. Two developments are reported here. First, a new scheme for accelerating and stabilising convergence by controlling step sizes is introduced. Second, a novel signal-variance based denoising function is proposed. Estimates of variances of different source are whit...

Removal of noise from an image is an extensively studied problem in image processing. Indeed, the 5 recent advent of sophisticated and highly effective denoising algorithms lead some to believe that 6 existing methods are touching the ceiling in terms of noise removal performance. Can we leverage 7 this impressive achievement to treat other tasks in image processing? Recent work has answered th...

We present a general wavelet-based denoising scheme for functional neuroimages and compare it to Gaussian smoothing, the standard method in functional neuroimaging. We adapted WaveLab thresholding routines to 2D data, and tested their effect on the signal-to-noise ratio of noisy images. In a simulated time series test, we also investigated the shapes of detected activations after denoising. key...

We look at the problem of denoising a spectrum arising from a mass spectrometer. In order to achieve this, we develop a model which describes the chemical noise using a stochastic differential equation. This model then allows us to use a localized image denoising algorithm to achieve improved denoising. It also is able to predict instrument error and generate additional spectra for simulation p...

Based on the wavelet transform principle of nonlinear signal denoising , a design of fault signal denoising of permanent magnet linear synchronous motor vertical elevating system is presented using Digital Signal Processor(DSP) chip as its detecting and processing core . The experimental results show that the wavelet transform fault signal denoising applied to this system can not only preserve ...

State-of-the-art denoising methods achieve impressive results, even for large noise levels. However, they can not be implemented in camera hardware, mainly due to the fact that they are computationally too intensive. The aim of this paper is then to show that we can obtain comparable denoising results to the ones obtained with state-of-art methods by inserting a well-chosen fast denoising metho...

In this paper we propose a generic recursive algorithm for improving image denoising methods. Given the initial denoised image, we suggest repeating the following “SOS” procedure: (i) Strengthen the signal by adding the previous denoised image to the degraded input image, (ii) Operate the denoising method on the strengthened image, and (iii) Subtract the previous denoised image from the restore...

We address the problem of image denoising in additive white noise without placing restrictive assumptions on its statistical distribution. In the recent literature, specific noise distributions have been considered and correspondingly, optimal denoising techniques have been developed. One of the successful approaches for denoising relies on the notion of unbiased risk estimation, which enables ...