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A mainstream type of the state of the arts (SOTAs) based on convolutional neural network (CNN) for real image denoising contains two sub-problems, i.e., noise estimation and non-blind denoising. This paper considers real noise approximated…

Image and Video Processing · Electrical Eng. & Systems 2022-11-29 Yifan Zuo , Jiacheng Xie , Yuming Fang , Yan Huang , Wenhui Jiang

Inferring a process matrix characterizing a quantum channel from experimental measurements is a key issue of quantum information. Sometimes the noise affecting the measured counts brings to matrices very different from the expected ones and…

Quantum Physics · Physics 2024-01-31 Massimiliano Guarneri , Andrea Chiuri

We introduce a new quantum noise deconvolution technique that does not rely on the complete knowledge of noise and does not require partial noise tomography. In this new method, we construct a set of observables with completely correctable…

Quantum Physics · Physics 2025-06-10 Nahid Ahmadvand , Laleh Memarzadeh

Spectral densities encode the relevant information characterising the system-environment interaction in an open-quantum system problem. Such information is key to determining the system's dynamics. In this work, we leverage the potential of…

Quantum Physics · Physics 2024-03-13 Jessica Barr , Giorgio Zicari , Alessandro Ferraro , Mauro Paternostro

A white noise analysis of modern deep neural networks is presented to unveil their biases at the whole network level or the single neuron level. Our analysis is based on two popular and related methods in psychophysics and neurophysiology…

Computer Vision and Pattern Recognition · Computer Science 2019-12-30 Ali Borji , Sikun Lin

We present a scalable method for learning local quantum channels using local expectation values measured on a single state -- their steady state. Our method is inspired by the algorithms for learning local Hamiltonians from their ground…

Quantum Physics · Physics 2024-07-12 Yigal Ilin , Itai Arad

Entanglement is a key quantity for characterizing quantum correlations in particle scattering processes, but its direct evaluation is computationally demanding on quantum hardware. In this work, we investigate whether fermion density…

Quantum Physics · Physics 2026-04-08 Hala Elhag , Yahui Chai

Deep neural networks (DNNs) have achieved remarkable success across diverse domains, but their performance can be severely degraded by noisy or corrupted training data. Conventional noise mitigation methods often rely on explicit…

Machine Learning · Computer Science 2025-06-16 Deliang Jin , Gang Chen , Shuo Feng , Yufeng Ling , Haoran Zhu

In many data analysis applications the following scenario is commonplace: we are given a point set that is supposed to sample a hidden ground truth $K$ in a metric space, but it got corrupted with noise so that some of the data points lie…

Computational Geometry · Computer Science 2017-03-28 Mickaël Buchet , Tamal K. Dey , Jiayuan Wang , Yusu Wang

Compared with traditional seismic noise attenuation algorithms that depend on signal models and their corresponding prior assumptions, removing noise with a deep neural network is trained based on a large training set, where the inputs are…

Geophysics · Physics 2019-07-23 Siwei Yu , Jianwei Ma , Wenlong Wang

In this work, we present tensor-based linear and nonlinear models for hyperspectral data classification and analysis. By exploiting principles of tensor algebra, we introduce new classification architectures, the weight parameters of which…

Computer Vision and Pattern Recognition · Computer Science 2018-12-26 Konstantinos Makantasis , Anastasios Doulamis , Nikolaos Doulamis , Antonis Nikitakis

Motivated by recent experiments with Josephson-junction circuits we reconsider decoherence effects in quantum two-level systems (TLS). On one hand, the experiments demonstrate the importance of 1/f noise, on the other hand, by operating at…

Mesoscale and Nanoscale Physics · Physics 2007-05-23 Alexander Shnirman , Yuriy Makhlin , Gerd Schön

Recent advances in machine learning have become increasingly popular in the applications of phase transitions and critical phenomena. By machine learning approaches, we try to identify the physical characteristics in the two-dimensional…

Disordered Systems and Neural Networks · Physics 2021-01-25 Shu Cheng , Fei He , Huai Zhang , Ka-Di Zhu , Yaolin Shi

We propose a fully-convolutional neural-network architecture for image denoising which is simple yet powerful. Its structure allows to exploit the gradual nature of the denoising process, in which shallow layers handle local noise…

Computer Vision and Pattern Recognition · Computer Science 2018-10-17 Tal Remez , Or Litany , Raja Giryes , Alex M. Bronstein

We propose a completely unsupervised method to understand audio scenes observed with random microphone arrangements by decomposing the scene into its constituent sources and their relative presence in each microphone. To this end, we…

Sound · Computer Science 2019-09-30 Jonah Casebeer , Michael Colomb , Paris Smaragdis

Convolutional Neural Network (CNN) recognition rates drop in the presence of noise. We demonstrate a novel method of counteracting this drop in recognition rate by adjusting the biases of the neurons in the convolutional layers according to…

Computer Vision and Pattern Recognition · Computer Science 2017-02-06 James R. Geraci , Parichay Kapoor

We propose a simple method for estimating noise level from a single color image. In most image-denoising algorithms, an accurate noise-level estimate results in good denoising performance; however, it is difficult to estimate noise level…

Computer Vision and Pattern Recognition · Computer Science 2019-04-05 Akihiro Nakamura , Michihiro Kobayashi

Procedural noise is a fundamental component of computer graphics pipelines, offering a flexible way to generate textures that exhibit "natural" random variation. Many different types of noise exist, each produced by a separate algorithm. In…

Dedicated analog neurocomputing circuits are promising for high-throughput, low power consumption applications of machine learning (ML) and for applications where implementing a digital computer is unwieldy (remote locations; small, mobile,…

Neural and Evolutionary Computing · Computer Science 2025-11-18 Ye min Thant , Methawee Nukunudompanich , Chu-Chen Chueh , Manabu Ihara , Sergei Manzhos

We study the problem of learning neural text classifiers without using any labeled data, but only easy-to-provide rules as multiple weak supervision sources. This problem is challenging because rule-induced weak labels are often noisy and…

Computation and Language · Computer Science 2021-03-12 Wendi Ren , Yinghao Li , Hanting Su , David Kartchner , Cassie Mitchell , Chao Zhang
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