Výsledky vyhledávání - Residual learning convolutional denoising autoencoder*

Enhanced Noise Reduction in Photoplethysmography Signals Using a Denoising Autoencoder.

Autoři: Shin-Chi Lai Yao-Feng Liang Yi-Chang Zhu a další

Zdroj: Sensors & Materials; 2025, Vol. 37 Issue 11, Part 3, p5123-5139, 17p

Témata: PHOTOPLETHYSMOGRAPHY, CONVOLUTIONAL neural networks, NOISE control, SIGNAL processing, SIGNAL denoising, REAL-time computing, MEDICAL technology, WEARABLE technology

Fault Diagnosis of Rolling Bearings Based on a Residual Dilated Pyramid Network and Full Convolutional Denoising Autoencoder

Autoři: Hongmei Shi Jingcheng Chen Jin Si a další

Zdroj: Sensors, Vol 20, Iss 20, p 5734 (2020)

Témata: intelligent fault diagnosis, residual learning, dilated pyramid network, fully convolutional denoising autoencoder, noise robustness, Chemical technology, TP1-1185

Popis souboru: electronic resource

Relation: https://www.mdpi.com/1424-8220/20/20/5734; https://doaj.org/toc/1424-8220

Přístupová URL adresa: https://doaj.org/article/995f8a47d49c4e059e88abcfd858ecc7

Dual Channel Residual Learning for Denoising Path Tracing.

Autoři: Liu, Ping Ji, Hangyu

Zdroj: International Journal of Image & Graphics; Jan2025, Vol. 25 Issue 1, p1-20, 20p

Témata: CONVOLUTIONAL neural networks, IMAGE denoising, ADAPTIVE filters

An improved autoencoder for denoising acoustic emission signals in rock fracturing.

Autoři: Wang, Tingting Qin, Yifan Zhao, Wanchun a další

Zdroj: Nondestructive Testing & Evaluation; Jun2025, Vol. 40 Issue 6, p2572-2597, 26p

Témata: ACOUSTIC emission, AUTOENCODERS, STANDARD deviations, DEEP learning, SIGNAL-to-noise ratio, EMISSION control, ACOUSTIC emission testing

Self‐supervised representation learning of metro interior noise based on variational autoencoder and deep embedding clustering.

Autoři: Wang, Yang Xiao, Hong Zhang, Zhihai a další

Zdroj: Computer-Aided Civil & Infrastructure Engineering; 2/4/2025, Vol. 40 Issue 4, p503-522, 20p

Témata: TRANSPORTATION noise, SIGNAL denoising, PATTERN perception, FAULT diagnosis, LATENT variables, MACHINE learning, EMPIRICAL research

MRI Rician Noise Reduction Using Recurrent Convolutional Neural Networks

Autoři: Javier Gurrola-Ramos Teresa Alarcon Oscar Dalmau a další

Přispěvatelé: Javier Gurrola-Ramos Teresa Alarcon Oscar Dalmau a další

Zdroj: RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
Universitat Politècnica de València (UPV)
IEEE Access, Vol 12, Pp 128272-128284 (2024)

Témata: Denoising, recurrent convolutional neural network, convolutional neural network, gated recurrent units, Computational modeling, Convolutional neural network, Autoencoder, Gated recurrent units, MRI denoising, TK1-9971, Kernel, Magnetic resonance imaging, Recurrent neural networks, Encoding, denoising, CIENCIAS DE LA COMPUTACION E INTELIGENCIA ARTIFICIAL, Training, Recurrent convolutional neural network, Convolutional neural networks, Electrical engineering. Electronics. Nuclear engineering, Noise reduction

Popis souboru: application/pdf

Přístupová URL adresa: https://riunet.upv.es/handle/10251/211402
https://doaj.org/article/4f30db8383fe42408584bc78991adcc5
https://hdl.handle.net/10251/211402
https://doi.org/10.1109/access.2024.3446791

Wireless channel corrupted image denoising using residual learning of adaptive wavelet with dilated deep convolutional neural network.

Autoři: Sreedhar, M. Aparna, K.

Zdroj: Signal, Image & Video Processing; Apr2024, Vol. 18 Issue 3, p2309-2321, 13p

Network security situation assessment method based on ACDAE-ResBiGRU.

Autoři: Li, Binyong Zhang, Jie

Zdroj: Maintenance & Reliability / Eksploatacja i Niezawodność; 2025, Vol. 27 Issue 4, p1-19, 19p

Témata: COMPUTER network security, FEATURE extraction, SITUATIONAL awareness, RISK assessment, RECURRENT neural networks, CLASSIFICATION, ARTIFICIAL neural networks, SIGNAL denoising

Medical image denoising using convolutional neural network: a residual learning approach.

Autoři: Jifara, Worku Jiang, Feng Rho, Seungmin a další

Zdroj: Journal of Supercomputing; Feb2019, Vol. 75 Issue 2, p704-718, 15p

Témata: IMAGE denoising, DIAGNOSTIC imaging, ARTIFICIAL neural networks, IMAGE quality analysis, SIGNAL-to-noise ratio

Automatic Fabric Defect Detection with a Multi-Scale Convolutional Denoising Autoencoder Network Model

Autoři: Shuang Mei Yudan Wang Guojun Wen

Zdroj: Sensors, Vol 18, Iss 4, p 1064 (2018)

Témata: fabric defect detection, unsupervised learning, deep neural network, convolutional denoising autoencoder, Gaussian pyramid, Chemical technology, TP1-1185

Popis souboru: electronic resource

Relation: http://www.mdpi.com/1424-8220/18/4/1064; https://doaj.org/toc/1424-8220

Přístupová URL adresa: https://doaj.org/article/5e389f2bca1348e780347c16d402a82d

Denoising and Baseline Correction Methods for Raman Spectroscopy Based on Convolutional Autoencoder: A Unified Solution.

Autoři: Han, Ming Dang, Yu Han, Jianda

Zdroj: Sensors (14248220); May2024, Vol. 24 Issue 10, p3161, 15p

Témata: RAMAN spectroscopy, CONVOLUTIONAL neural networks, IMAGE denoising, NOISE control, SIGNAL processing

One-Dimensional Residual Convolutional Autoencoder Based Feature Learning for Gearbox Fault Diagnosis.

Autoři: Yu, Jianbo Zhou, Xingkang

Zdroj: IEEE Transactions on Industrial Informatics; Oct2020, Vol. 16 Issue 10, p6347-6358, 12p

Single Image Super Resolution Using Deep Residual Learning.

Autoři: Hassan, Moiz Illanko, Kandasamy Fernando, Xavier N.

Zdroj: AI; Mar2024, Vol. 5 Issue 1, p426-445, 20p

Témata: DEEP learning, COMPUTER vision, DATABASES, REMOTE-sensing images, CONVOLUTIONAL neural networks

Robust correlation mapping of train-induced stresses for high-speed railway bridge using convolutional denoising autoencoder.

Autoři: Niu, Jin Li, Zhonglong Zhuo, Yi a další

Zdroj: Structural Health Monitoring; Mar2023, Vol. 22 Issue 2, p832-845, 14p

Témata: RAILROAD bridges, STRUCTURAL health monitoring, TRUSS bridges, DEEP learning, DATA integrity, MISSING data (Statistics), SENSOR networks, HIGH speed trains, FEATURE extraction

Deep Convolutional Denoising Autoencoders with Network Structure Optimization for the High-Fidelity Attenuation of Random GPR Noise

Autoři: Deshan Feng Xiangyu Wang Xun Wang a další

Zdroj: Remote Sensing, Vol 13, Iss 9, p 1761 (2021)

Témata: GPR, noise attenuation, Gaussian spike impulse noise, deep convolutional denoising autoencoders (CDAEs), deep convolutional denoising autoencoders with network structure optimization (CDAEsNSO), Science

Popis souboru: electronic resource

Relation: https://www.mdpi.com/2072-4292/13/9/1761; https://doaj.org/toc/2072-4292

Přístupová URL adresa: https://doaj.org/article/c42d57a98e7f4b01ba98105ec0458aaa

Denoising of 3D Brain MR Images with Parallel Residual Learning of Convolutional Neural Network Using Global and Local Feature Extraction.

Autoři: Wu, Liang Hu, Shunbo Liu, Changchun

Zdroj: Computational Intelligence & Neuroscience; 5/4/2021, p1-18, 18p

Témata: IMAGE denoising, CONVOLUTIONAL neural networks, MAGNETIC resonance imaging, FEATURE extraction, NOISE pollution, BRAIN imaging

Perceptual adversarial non-residual learning for blind image denoising.

Autoři: Khan, Aamir Jin, Weidong Naqvi, Rizwan Ali

Zdroj: Soft Computing - A Fusion of Foundations, Methodologies & Applications; Aug2022, Vol. 26 Issue 16, p7933-7957, 25p

Témata: IMAGE denoising, CONVOLUTIONAL neural networks, IMAGE processing, GENERATIVE adversarial networks

DespNet: A residual learning based deep convolutional neural network for the despeckling of optical coherence tomography images.

Autoři: P. S., Arun P. Gopi, Varun

Zdroj: Multimedia Tools & Applications; Apr2024, Vol. 83 Issue 13, p39961-39981, 21p

Research on Loosening Fault Diagnosis Method of Escalator Drive Mainframe Anchor Bolts Based on Improved High-Strength Denoising RCDAE Model.

Autoři: Chen, Dongdong Chen, Minghui Lang, Binxin a další

Zdroj: Sensors (14248220); Sep2025, Vol. 25 Issue 17, p5219, 23p

Témata: ANCHOR bolts (Structural engineering), FAULT diagnosis, ESCALATORS, SIGNAL denoising, LOSS functions (Statistics), FEATURE extraction, NOISE

An Efficient Convolutional Denoising Autoencoder-Based BDS NLOS Detection Method in Urban Forest Environments.

Autoři: Yahang Qin Zhenni Li Shengli Xie a další

Zdroj: Sensors (14248220); 3/15/2024, Vol. 24 Issue 6, p1-23, 23p, 1 Color Photograph, 2 Diagrams, 4 Charts, 8 Graphs