Search Results - noise learning based denoising autoencoder*

Noise Learning-Based Denoising Autoencoder

Authors: Woong-Hee Lee Mustafa Ozger Ursula Challita et al.

Source: Lee, W H, Ozger, M, Challita, U & Sung, K W 2021, 'Noise Learning-Based Denoising Autoencoder', IEEE Communications Letters, vol. 25, no. 9, 9462839, pp. 2983-2987. https://doi.org/10.1109/LCOMM.2021.3091800
IEEE Communications Letters

Subject Terms: Signal Processing (eess.SP), FOS: Computer and information sciences, Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Noise learning based denoising autoencoder, Precise localization, 02 engineering and technology, Machine Learning (cs.LG), Artificial Intelligence (cs.AI), Symbol demodulation, Machine learning, FOS: Electrical engineering, electronic engineering, information engineering, 0202 electrical engineering, electronic engineering, information engineering, Electrical Engineering and Systems Science - Signal Processing, Signal restoration

Access URL: https://ieeexplore.ieee.org/ielx7/4234/9531652/09462839.pdf
http://arxiv.org/abs/2101.07937
https://ieeexplore.ieee.org/document/9462839
https://dblp.uni-trier.de/db/journals/corr/corr2101.html#abs-2101-07937

NOISE LEARNING-BASED DENOISING AUTOENCODER

Noise Learning-Based Denoising Autoencoder

Authors: Lee, Woong-Hee Ozger, Mustafa Challita, Ursula et al.

Index Terms: Noise reduction, Training, Noise measurement, Random variables, Encoding, Decoding, Internet of Things, Machine learning, noise learning based denoising autoencoder, signal restoration, symbol demodulation, precise localization, Signal Processing, Signalbehandling, Telecommunications, Telekommunikation, Article in journal, info:eu-repo/semantics/article, text

URL: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-302636
IEEE Communications Letters, 1089-7798, 2021, 25:9, s. 2983-2987

A Deep-learning-based Auto Encoder-Decoder Model for Denoising Electrocardiogram Signals.

Authors: Das, Maumita Sahana, Bikash Chandra

Source: IETE Journal of Research. Jan-Jun2025, Vol. 71 Issue 1, p326-340. 15p.

Subject Terms: *CONVOLUTIONAL neural networks, *ADDITIVE white Gaussian noise, *MEAN square algorithms, *SIGNAL-to-noise ratio, *SIGNAL denoising

Development of machine learning-based analysis and processing tools applied to magnetization transfer MRI: Z-spectral denoising and fitting

Authors: Mohammed Ali, Sajad Lund University, Faculty of Science, Medical Radiation Physics, Lund, Lunds universitet, Naturvetenskapliga fakulteten, Medicinsk strålningsfysik, Lund, Originator Lund University, Faculty of Science, Medical Radiation Physics, Lund, MR Physics, Lunds universitet, Naturvetenskapliga fakulteten, Medicinsk strålningsfysik, Lund, MR Physics, Originator et al.

Contributors: Mohammed Ali, Sajad Lund University, Faculty of Science, Medical Radiation Physics, Lund, Lunds universitet, Naturvetenskapliga fakulteten, Medicinsk strålningsfysik, Lund, Originator Lund University, Faculty of Science, Medical Radiation Physics, Lund, MR Physics, Lunds universitet, Naturvetenskapliga fakulteten, Medicinsk strålningsfysik, Lund, MR Physics, Originator et al.

Subject Terms: Medical and Health Sciences, Other Medical and Health Sciences, Other Medical and Health Sciences not elsewhere specified, Medicin och hälsovetenskap, Annan medicin och hälsovetenskap, Övrig annan medicin och hälsovetenskap, Natural Sciences, Computer and Information Sciences, Artificial Intelligence, Naturvetenskap, Data- och informationsvetenskap (Datateknik), Artificiell intelligens, Engineering and Technology, Medical Engineering, Medical Imaging, Teknik, Medicinteknik, Medicinsk bildvetenskap

File Description: electronic

Access URL: https://lucris.lub.lu.se/ws/files/227931572/Thesis_V5_with_cover.pdf

LN-MRSCAE: A novel deep learning based denoising method for mechanical vibration signals.

Authors: Du, Wenliao Yang, Lingkai Wang, Hongchao et al.

Source: Journal of Vibration & Control. Feb2024, Vol. 30 Issue 3/4, p459-471. 13p.

Subject Terms: *DEEP learning, *VIBRATION (Mechanics), *LEARNING modules, *SIGNAL denoising

The MLDAR Model: Machine Learning-Based Denoising of Structural Response Signals Generated by Ambient Vibration.

Authors: Damikoukas, Spyros Lagaros, Nikos D.

Source: Computation; Feb2024, Vol. 12 Issue 2, p31, 25p

Subject Terms: STRUCTURAL health monitoring, ELECTRONIC noise, IMAGE denoising, SIGNAL-to-noise ratio, EIGENFREQUENCIES, MACHINE learning, DEEP learning, ELECTRIC oscillators

Deep Learning-Based Denoising of CEST MR Data: A Feasibility Study on Applying Synthetic Phantoms in Medical Imaging.

Authors: Radke, Karl Ludger Kamp, Benedikt Adriaenssens, Vibhu et al.

Source: Diagnostics (2075-4418). Nov2023, Vol. 13 Issue 21, p3326. 20p.

Subject Terms: *NOISE control, *DIAGNOSTIC imaging, *SIGNAL-to-noise ratio, *MAGNETIC resonance imaging, *MAGNETIZATION transfer, *SIGNAL convolution, *PROSODIC analysis (Linguistics)

Deep Learning-Based Denoising for Optical Coherence Tomography: Evaluating Self-Supervised and Generative Models Across Retinal Datasets

Authors: Diogen BABUC Alesia LOBONŢ Alexandru FARCAŞ et al.

Source: Applied Medical Informatics, Vol 47, Iss Suppl. 1 (2025)

Subject Terms: OCT Denoising, Deep Learning in Medical Imaging, Retinal Disease Detection, Self-Supervised Learning, Generative Adversarial Network, Computer applications to medicine. Medical informatics, R858-859.7

File Description: electronic resource

Relation: https://ami.info.umfcluj.ro/index.php/AMI/article/view/1124; https://doaj.org/toc/2067-7855

Access URL: https://doaj.org/article/223253d344d84d6e8d80692312a7024a

EFFECT OF NOISE REDUCTION ON PLSR MODELING IN NEAR INFRARED SPECTROSCOPY USING DENOISING AUTOENCODER.

Authors: ÇATALTAŞ, Özcan

Source: New Trends in Computer Sciences; 2025, Vol. 3 Issue 1, p38-48, 11p

Subject Terms: PARTIAL least squares regression, STANDARD deviations, SIGNAL-to-noise ratio, NEAR infrared spectroscopy, AUTOENCODERS

A hybrid deep learning framework for denoising acoustic partial discharge signals.

Authors: Kumar, Chandan Ganguly, Biswarup Dey, Debangshu et al.

Source: Signal, Image & Video Processing; Dec2025, Vol. 19 Issue 12, p1-17, 17p

Deep Learning-Based Denoising of Noisy Vibration Signals from Wavefront Sensors Using BiL-DCAE.

Authors: Pan, Yun Luo, Quan Fan, Yiyou et al.

Source: Sensors (14248220); Aug2025, Vol. 25 Issue 16, p5012, 35p

Subject Terms: SIGNAL denoising, WAVEFRONT sensors, DEEP learning, VIBRATION measurements, GEOPHYSICAL prospecting, LONG short-term memory, NOISE control

Improving OCTA Image Quality for Alzheimer's Diagnosis Using Autoencoder, Self-Attention and GANs.

Authors: C. K., Madhu C. V., Aravinda S. V., Aruna Kumar et al.

Source: International Journal of Intelligent Engineering & Systems; 2025, Vol. 18 Issue 7, p1-17, 17p

Subject Terms: GENERATIVE adversarial networks, ALZHEIMER'S disease, SPECKLE interference, OPTICAL coherence tomography, AUTOENCODERS, DEEP learning

Innovative Noise Extraction and Denoising in Low-Dose CT Using a Supervised Deep Learning Framework.

Authors: Zhang, Wei Salmi, Abderrahmane Yang, Chifu et al.

Source: Electronics (2079-9292); Aug2024, Vol. 13 Issue 16, p3184, 22p

Subject Terms: CONVOLUTIONAL neural networks, GENERATIVE adversarial networks, NOISE control, SUPERVISED learning, SIGNAL-to-noise ratio, DEEP learning

BA-ATEMNet: Bayesian Learning and Multi-Head Self-Attention for Theoretical Denoising of Airborne Transient Electromagnetic Signals.

Authors: Wang, Weijie Wang, Xuben Yu, Xiaodong et al.

Source: Sensors (14248220); Jan2025, Vol. 25 Issue 1, p77, 27p

Subject Terms: CONVOLUTIONAL neural networks, ELECTRIC transients, SIGNAL denoising, SIGNAL-to-noise ratio, PROSPECTING, DEEP learning

Deep Learning-Based Denoising for Interactive Realistic Rendering of Biomedical Volumes.

Authors: Denisova, Elena Bocchi, Leonardo Nardi, Cosimo

Source: Applied Sciences (2076-3417); Sep2025, Vol. 15 Issue 18, p9893, 21p

Subject Terms: DEEP learning, SIGNAL denoising, COMPUTER-assisted image analysis (Medicine), VISUALIZATION, RENDERING (Computer graphics), RAY tracing, IMAGE quality analysis

A deep learning framework for suppressing prestack seismic random noise without noise-free labels.

Authors: Han Wang Jie Zhang

Source: Energy Geoscience; Jul2024, Vol. 5 Issue 3, p1-14, 14p

Subject Terms: DEEP learning, RANDOM noise theory, SIGNAL denoising, SUPERVISED learning, MONTE Carlo method

Artificial Neural Networks in the Wastewater Industry. From Conventional to Data-based Industrial Control

Authors: Pisa Dacosta, Ivan

Thesis Advisors: Morell Pérez, Antoni, López Vicario, José, Vilanova i Arbós, Ramon

Source: TDX (Tesis Doctorals en Xarxa)

Subject Terms: Xarxes neuronals artificials, Redes neuronales artificiales, Artificial neural networks, Control automàtic, Control automático, Automatic control, Estacions depuradores d'aigües residuals, Estaciones depuradoras de aguas residuales, Wastewater treatment plants, Tecnologies

Time: 621.3

File Description: application/pdf

Access URL: http://hdl.handle.net/10803/675219

Self‐supervised structural similarity‐based convolutional neural network for cardiac diffusion tensor image denoising.

Authors: Yuan, Nannan Wang, Lihui Ye, Chen et al.

Source: Medical Physics; Oct2023, Vol. 50 Issue 10, p6137-6150, 14p

Subject Terms: CONVOLUTIONAL neural networks, DIFFUSION tensor imaging, IMAGE denoising, MYOCARDIUM, SUPERVISED learning, HEART, STANDARD deviations, SIGNAL-to-noise ratio

Machine Learning-Based Denoising Techniques for Monte Carlo Rendering: A Literature Review.

Authors: Liew Wen Yen Thinakaran, Rajermani Somasekar, J.

Source: International Journal of Advanced Computer Science & Applications; Feb2025, Vol. 16 Issue 2, p581-588, 8p