Results 261 to 270 of about 36,871 (305)

Multicomponent signal denoising with synchrosqueezing

2012 IEEE Statistical Signal Processing Workshop (SSP), 2012
In this paper, we develop a new technique based on the synchrosqueezing method to denoise multicomponent signals. The approach proposed is based on a two step strategy: a mode detection step followed by a reconstruction one. The emphasis is put on the robustness of the detection step in a noisy context, a key issue in the implementation of the method ...
Sylvain Meignen, Thomas Oberlin, Stephen McLaughlin 0001   +2 more
openaire   +1 more source

Nonlocal Means Denoising of ECG Signals

IEEE Transactions on Biomedical Engineering, 2012
Patch-based methods have attracted significant attention in recent years within the field of image processing for a variety of problems including denoising, inpainting, and super-resolution interpolation. Despite their prevalence for processing 2-D signals, they have received little attention in the 1-D signal processing literature.
Brian H. Tracey, Eric L. Miller 0001
openaire   +2 more sources

A stacked contractive denoising auto-encoder for ECG signal denoising

Physiological Measurement, 2016
As a primary diagnostic tool for cardiac diseases, electrocardiogram (ECG) signals are often contaminated by various kinds of noise, such as baseline wander, electrode contact noise and motion artifacts. In this paper, we propose a contractive denoising technique to improve the performance of current denoising auto-encoders (DAEs) for ECG signal ...
Peng, Xiong, Hongrui, Wang, Ming, Liu, Feng, Lin, Zengguang, Hou, Xiuling, Liu   +5 more
openaire   +2 more sources

Signal and image denoising without regularization

2013 IEEE International Conference on Image Processing, 2013
This paper proposes a novel approach for image and signal denoising that does not need any classical regularization. It subtracts a sorted realization of noise to the sorted noisy signal. The similarity between the noise that corrupted the signal and the selected noise realization allows us to denoise monotonic signals.
BRUNI, VITTORIA, D. Vitulano
openaire   +4 more sources

A Fast Scheme for Multiscale Signal Denoising

2008
This paper exploits the time scale structure of the wavelet coefficients for implementing a novel and fast scheme for signal and image denoising. The time scale behavior of the coefficients is rigorously modeled through superposition of simple atoms using suitable projection spaces. This result allows us to avoid expensive numerical schemes requiring a
BRUNI, VITTORIA, Benedetto Piccoli, Domenico Vitulano   +2 more
openaire   +1 more source

The effect of denoising on classification of ECG signals

2015 XXV International Conference on Information, Communication and Automation Technologies (ICAT), 2015
Electrocardiogram (ECG) signals are affected by different types of noise and artifacts which can hide significant information related to heart functioning. Therefore, denoising of signals is essential for accurate diagnosis of different heart diseases.
Emina Alickovic, Zdenka Babic
openaire   +1 more source

Ultrasonic signal denoising based on autoencoder

Review of Scientific Instruments, 2020
At present, denoising parameters in different signal processing algorithms require a specific signal waveform to be set. Human factors would significantly affect the denoising result. To solve this problem, we proposed a signal adaptive denoising method based on a denoising autoencoder to achieve denoising on ultrasonic signals. By applying this method
Fei Gao, Bing Li, Lei Chen, Xiang Wei, Zhongyu Shang, Chen He   +5 more
openaire   +2 more sources

Neighboring Coefficients Preservation for Signal Denoising

Circuits, Systems, and Signal Processing, 2011
For traditional thresholding denoising, the wavelet coefficients are thresholded without considering the information of other coefficients. In this paper, we propose a novel denoising approach which incorporates the neighboring coefficients into signal denoising.
Ying Yang, Yusen Wei
openaire   +1 more source

RunDAE model: Running denoising autoencoder models for denoising ECG signals

Computers in Biology and Medicine, 2023
The denoising autoencoder (DAE) is commonly used to denoise bio-signals such as electrocardiogram (ECG) signals through dimensional reduction. Typically, the DAE model needs to be trained using correlated input segments such as QRS-aligned segments or long ECG segments. However, using long ECG segments as an input can result in a complex deep DAE model
Fars Samann, Thomas Schanze
openaire   +2 more sources

A stochastic diffusion approach to signal denoising

1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258), 1999
We present a stochastic formulation of a linear diffusion equation (or heat equation), and in light of the potential applications ranging from signal denoising to image enhancement/segmentation of its nonlinear extensions, we propose a more general nonlinear stochastic diffusion.
Hamid Krim, Yufang Bao
openaire   +1 more source