深度声场降噪器：利用深度神经网络对光学测量的声场进行降噪

通过智能分区实现深度EEG去噪器的无监督训练

Enabling Unsupervised Training of Deep EEG Denoisers With Intelligent Partitioning

本文提出iPSD方法，通过学习将输入EEG段分割为独立的噪声实现，实现无监督去噪，尤其在低信噪比和复杂噪声下表现优异。

通过展开图拉普拉斯正则化器构建可解释的深度去噪器

Constructing an Interpretable Deep Denoiser by Unrolling Graph Laplacian Regularizer

本研究提出通用框架，通过展开带图拉普拉斯正则化的MAP问题解构建可解释图深度去噪器GDD，其参数量少、易初始化，去噪性能媲美现有方法且协变量偏移鲁棒性更强。

Interferometric Passive Radar Imaging with Deep Denoising Priors

Passive radar has key advantages over its active counterpart in terms of cost and stealth. In this paper, we address passive radar imaging problem by interferometric inversion using a spectral estimation method with a priori information within a deep learning (DL) framework. Cross-correlating the received signals from different look directions mitigates the influence of shared transmitter related phase components despite lack of a cooperative transmitter, and permits tractable inference via interferometric inversion. We thereon leverage deep architectures for modeling a priori information and …

含噪神经网络去噪：一种带补偿的贝叶斯方法

Denoising Noisy Neural Networks: A Bayesian Approach with Compensation

针对噪声环境下产生的含噪神经网络权重重建问题，本文提出一种带补偿的贝叶斯去噪方法，在理论和多类实验中均证明其性能显著优于传统最大似然估计。

Solving RED with Weighted Proximal Methods

REgularization by Denoising (RED) is an attractive framework for solving inverse problems by incorporating state-of-the-art denoising algorithms as the priors. A drawback of this approach is the high computational complexity of denoisers, which dominate the computation time. In this paper, we apply a general framework called weighted proximal methods (WPMs) to solve RED efficiently. We first show that two recently introduced RED solvers (using the fixed point and accelerated proximal gradient methods) are particular cases of WPMs. Then we show by numerical experiments that slightly more sophis…

Perceptual-based deep-learning denoiser as a defense against adversarial attacks on ASR systems

In this paper we investigate speech denoising as a defense against adversarial attacks on automatic speech recognition (ASR) systems. Adversarial attacks attempt to force misclassification by adding small perturbations to the original speech signal. We propose to counteract this by employing a neural-network based denoiser as a pre-processor in the ASR pipeline. The denoiser is independent of the downstream ASR model, and thus can be rapidly deployed in existing systems. We found that training the denoisier using a perceptually motivated loss function resulted in increased adversarial robustne…

D-VDAMP: Denoising-based Approximate Message Passing for Compressive MRI

Plug and play (P&P) algorithms iteratively apply highly optimized image denoisers to impose priors and solve computational image reconstruction problems, to great effect. However, in general the "effective noise", that is the difference between the true signal and the intermediate solution, within the iterations of P&P algorithms is neither Gaussian nor white. This fact makes existing denoising algorithms suboptimal. In this work, we propose a CNN architecture for removing colored Gaussian noise and combine it with the recently proposed VDAMP algorithm, whose effective noise follows a predicta…

3D 立体接点场：一种噪声鲁棒、无需训练的结构先验用于体积分量逆问题

3D Field of Junctions: A Noise-Robust, Training-Free Structural Prior for Volumetric Inverse Problems

本文提出3D FoJ方法，通过优化3D楔形接点来实现体积分量逆问题的去噪，无需训练数据，保留和增强3D边缘和角结构，适用于低信噪比下的多种3D成像任务。

S2S-WTV：利用加权全变分正则化自监督学习进行地震数据噪声衰减

S2S-WTV: Seismic Data Noise Attenuation Using Weighted Total Variation Regularized Self-Supervised Learning

本文提出 S2S-WTV，在 Self2Self 自监督框架中引入按道掩码与加权全变分正则化，并用 ADMM 求解，仅依赖含噪数据即可稳健去噪并保持信号边缘。

Can learning from natural image denoising be used for seismic data interpolation?

We propose a convolutional neural network (CNN) denoising based method for seismic data interpolation. It provides a simple and efficient way to break though the lack problem of geophysical training labels that are often required by deep learning methods. The new method consists of two steps: (1) Train a set of CNN denoisers from natural image clean-noisy pairs to learn denoising; (2) Integrate the trained CNN denoisers into project onto convex set (POCS) framework to perform seismic data interpolation. The method alleviates the demanding of seismic big data with similar features as applicatio…

低复杂度波束域通道去噪算法用于毫米波大规模MIMO系统与低分辨率ADCs

Low-Complexity Beamspace Channel Denoiser for mmWave Massive MIMO with Low-Resolution ADCs

本文提出一种低复杂度的波束域通道去噪算法，针对毫米波大规模MIMO系统与低分辨率ADCs，利用波束域内毫米波信道的稀疏性，结合伯努利-复高斯先验模型，通过闭合形式阈值和硬阈值去噪规则实现高效去噪，降低计算复杂度并提升硬件部署效率。