Robust graph-filter identification with graph denoising regularization

Sparsity-Assisted Signal Denoising and Pattern Recognition in Time-Series Data

We address the problem of signal denoising and pattern recognition in processing batch-mode time-series data by combining linear time-invariant filters, orthogonal multiresolution representations, and sparsity-based methods. We propose a novel approach to designing higher-order zero-phase low-pass, high-pass, and band-pass infinite impulse response filters as matrices, using spectral transformation of the state-space representation of digital filters. We also propose a proximal gradient-based technique to factorize a special class of zero-phase high-pass and band-pass digital filters so that t…

Denoising ECG by Adaptive Filter with Empirical Mode Decomposition

Electrocardiogram (ECG) signal is an important physiological signal which contains cardiac information and is the basis to diagnosis cardiac related diseases. In this paper, several innovative and efficient methods based on adaptive filter and empirical mode decomposition (EMD) to denoise ECG signal contaminated by various kinds of noise, including baseline wander (BW), power line interference (PLI), electrode motion artifact (EM) and muscle artifact (MA), are proposed. We first present a novel method based on EMD and adaptive filter for the removal of BW and PLI in ECG signal. We then extend …

心脏起搏器去噪滤波器的ASIC实现

ASIC Implementation of Denoising Filters for Pacemakers

本文针对心脏起搏器电路中可植入QRS复合检测器，低通滤波器，采用流水线与折叠优化技术设计了ASIC，在功耗0.7575 mW下使面积减少48.37%。

A novel ECG signal denoising filter selection algorithm based on conventional neural networks

We propose a novel deep learning based denoising filter selection algorithm for noisy Electrocardiograph (ECG) signal preprocessing. ECG signals measured under clinical conditions, such as those acquired using skin contact devices in hospitals, often contain baseline signal disturbances and unwanted artefacts; indeed for signals obtained outside of a clinical environment, such as heart rate signatures recorded using non-contact radar systems, the measurements contain greater levels of noise than those acquired under clinical conditions. In this paper, we focus on heart rate signals acquired us…

Node-Adaptive Regularization for Graph Signal Reconstruction

A critical task in graph signal processing is to estimate the true signal from noisy observations over a subset of nodes, also known as the reconstruction problem. In this paper, we propose a node-adaptive regularization for graph signal reconstruction, which surmounts the conventional Tikhonov regularization, giving rise to more degrees of freedom; hence, an improved performance. We formulate the node-adaptive graph signal denoising problem, study its bias-variance trade-off, and identify conditions under which a lower mean squared error and variance can be obtained with respect to Tikhonov r…

有向图信号去噪的最优维纳滤波解

Optimal Wiener-Filter Solutions for Denoising of Graph Signals on Directed Graphs

针对有向图上的图信号，提出维纳滤波去噪方法，在多种平稳性假设下给出最优解，并在温度图上验证了有效性。

基于冗余技术与心电信号去噪的容错FPGA实现

Fault Tolerant FPGA Implementation on Redundancy Techniques and ECG Denoising

本文提出一种基于5模冗余的容错FPGA实现方法，结合FIR滤波器与心电信号去噪，以提高硬件系统在瞬态故障下的可靠性。

基于去噪正则化的少观测数据阵列SAR三维稀疏成像

Array SAR 3D Sparse Imaging Based on Regularization by Denoising Under Few Observed Data

针对少观测数据下稀疏成像目标信息丢失问题，提出基于去噪正则化（RED）和近端梯度下降类方法的阵列SAR 3D稀疏成像框架，利用去噪算子构建先验项，提高重建精度并保留结构信息，实验验证其优越性。

MR elasticity reconstruction using statistical physical modeling and explicit data-driven denoising regularizer

Elasticity image, visualizing the quantitative map of tissue stiffness, can be reconstructed by solving an inverse problem. Classical methods for magnetic resonance elastography (MRE) try to solve a regularized optimization problem comprising a deterministic physical model and a prior constraint as data-fidelity term and regularization term, respectively. For improving the elasticity reconstructions, appropriate prior about the underlying elasticity distribution is required which is not unique. This article proposes an infused approach for MRE reconstruction by integrating the statistical repr…

Gabor足矣：基于Gabor综合字典先验的可解释深度去噪

Gabor is Enough: Interpretable Deep Denoising with a Gabor Synthesis Dictionary Prior

本研究将去噪CNN的滤波器约束为可学习二维实Gabor滤波器，提出GDLNet，以极少参数取得接近SOTA的去噪性能，保留噪声泛化性，验证了低层视觉CNN表征可如Gabor滤波器组般简单可解释。

线性MMSE滤波器的自动正则化

Automatic Regularization for Linear MMSE Filters

本文针对线性MMSE滤波器设计中的正则化问题，提出一种基于贝叶斯方法从观测信号中自动确定正则化参数的方案，并在系统辨识和波束成形中验证其接近最优的性能。