Suchergebnisse - cnn overcomplete autoencoder reference errors analysis*

AESR3D: 3D overcomplete autoencoder for trabecular computed tomography super resolution.

Autoren: Zhang, Shuwei Liang, Yefeng Li, Xingyu et al.

Quelle: CAAI Transactions on Intelligence Technology; Jun2025, Vol. 10 Issue 3, p652-665, 14p

Schlagwörter: BONE densitometry, BONE density, AUTOENCODERS, COMPUTED tomography, CANCELLOUS bone, DEEP learning

Machine Learning Application in Different Imaging Modalities for Detection of Obstructive Coronary Artery Disease and Outcome Prediction: A Systematic Review and Meta-Analysis.

Autoren: McGranaghan, Peter Schoeppenthau, Doreen Popp, Antonia et al.

Quelle: Hearts; Sep2025, Vol. 6 Issue 3, p21, 16p

Overcomplete graph convolutional denoising autoencoder for noisy skeleton action recognition.

Autoren: Guo, Jiajun Ji, Qingge Shan, Guangwei

Quelle: IET Image Processing (Wiley-Blackwell); 1/10/2024, Vol. 18 Issue 1, p233-246, 14p

Schlagwörter: SIGNAL denoising, COMPUTER vision

ECG Signal Classification of Cardiovascular Disorder using CWT and DCNN.

Autoren: Rahman, Tawfikur Ahommed, Rasel Deb, Nibedita et al.

Quelle: Journal of Biomedical Physics & Engineering; Feb2025, Vol. 15 Issue 1, p77-92, 16p

Schlagwörter: CONVOLUTIONAL neural networks, SIGNAL classification, WAVELET transforms, CARDIOVASCULAR diseases, SIGNAL processing

A Comprehensive Review of GAN-Based Denoising Models for Low-Dose Computed Tomography Images.

Autoren: Sandhu, Manbir Kushwaha, Sumit Arora, Tanvi

Quelle: International Journal of Image & Graphics; Jul2025, Vol. 25 Issue 4, p1-38, 38p

Hydroelectric Unit Vibration Signal Feature Extraction Based on IMF Energy Moment and SDAE.

Autoren: Liu, Dong Kong, Lijun Yao, Bing et al.

Quelle: Water (20734441); Jul2024, Vol. 16 Issue 14, p1956, 15p

Schlagwörter: FEATURE extraction, FOURIER analysis, MOMENTS method (Statistics), FOURIER transforms, COUPLINGS (Gearing), HILBERT-Huang transform

Deep Medical Image Reconstruction with Autoencoders using Deep Boltzmann Machine Training.

Autoren: S., Saravanan Juliet, Sujitha

Quelle: EAI Endorsed Transactions on Pervasive Health & Technology; 2020, Vol. 6 Issue 24, p1-9, 9p

Schlagwörter: MACHINE learning, LOGISTIC regression analysis, STAKEHOLDERS, COMPARATIVE studies, ELECTRONIC health records

Current status and challenges in electroencephalography (EEG)-based driver fatigue detection: a comprehensive survey.

Autoren: Hassan, Jahid Naziullah, Shekh Rashid, Mamunur et al.

Quelle: Cognitive Neurodynamics; 9/1/2025, Vol. 19 Issue 1, p1-38, 38p

A Novel Hybrid Machine Learning-Based System Using Deep Learning Techniques and Meta-Heuristic Algorithms for Various Medical Datatypes Classification.

Autoren: Kadhim, Yezi Ali Guzel, Mehmet Serdar Mishra, Alok

Quelle: Diagnostics (2075-4418); Jul2024, Vol. 14 Issue 14, p1469, 25p

Schlagwörter: COMPUTER-aided diagnosis, METAHEURISTIC algorithms, CONVOLUTIONAL neural networks, DEEP learning, COMPUTER science

Model-based Bayesian Fusion-Net for infrared and visible image fusion.

Autoren: Li, Wang Yafang, Kuang Ziyi, Cai et al.

Quelle: EURASIP Journal on Image & Video Processing; 8/6/2025, Vol. 2025 Issue 1, p1-25, 25p

Schlagwörter: IMAGE fusion, INFRARED photography, COMPUTATIONAL complexity, QUANTITATIVE research, IMAGE processing

End-to-end algorithm research in PACT — from signal processing to reconstruction solution to image processing: A review.

Autoren: Cheng, JinJin Wang, Shuilin Dong, Shirui et al.

Quelle: Journal of Innovative Optical Health Sciences; Jul2025, Vol. 18 Issue 4, p1-43, 43p

Deep convolutional autoencoders as generic feature extractors in seismological applications.

Autoren: Qingkai Kong Andrea Chiang Aguiar, Ana C. et al.

Quelle: Artificial Intelligence in Geosciences; 2021, p96-106, 11p

Schlagwörter: SEISMOLOGY, FEATURE extraction, DEEP learning, CONVOLUTIONAL neural networks, MATHEMATICAL regularization

eXplainable AI for Word Embeddings: A Survey.

Autoren: Boselli, Roberto D'Amico, Simone Nobani, Navid

Quelle: Cognitive Computation; Feb2025, Vol. 17 Issue 1, p1-24, 24p

Efficient Diffusion Posterior Sampling for Noisy Inverse Problems.

Autoren: Li, Ji Wang, Chao

Quelle: SIAM Journal on Imaging Sciences; 2025, Vol. 18 Issue 2, p1468-1492, 25p

A systematic review of deep learning-based denoising for low-dose computed tomography from a perceptual quality perspective.

Autoren: Kim, Wonjin Jeon, Sun-Young Byun, Gyuri et al.

Quelle: Biomedical Engineering Letters; Nov2024, Vol. 14 Issue 6, p1153-1173, 21p

Unsupervised abnormality detection through mixed structure regularization (MSR) in deep sparse autoencoders.

Autoren: Freiman, Moti Manjeshwar, Ravindra Goshen, Liran

Quelle: Medical Physics; May2019, Vol. 46 Issue 5, p2223-2231, 9p

Schlagwörter: MATHEMATICAL regularization, ABNORMALITIES in animals, RANDOM noise theory, CORONARY disease, SPARSE approximations, DATA corruption, COMPUTED tomography

Spatiotemporal Behind-the-Meter Load and PV Power Forecasting via Deep Graph Dictionary Learning.

Autoren: Khodayar, Mahdi Liu, Guangyi Wang, Jianhui et al.

Quelle: IEEE Transactions on Neural Networks & Learning Systems; Oct2021, Vol. 32 Issue 10, p4713-4727, 15p

Schlagwörter: LOAD forecasting (Electric power systems), FORECASTING, FORECASTING methodology, PETRI nets, SMART meters, TIME series analysis, UNITS of measurement

A Hybrid Structural Sparsification Error Model for Image Restoration.

Autoren: Zha, Zhiyuan Wen, Bihan Yuan, Xin et al.

Quelle: IEEE Transactions on Neural Networks & Learning Systems; Sep2022, Vol. 33 Issue 9, p4451-4465, 15p

Schlagwörter: IMAGE reconstruction, DATA corruption, VISUAL perception, IMAGE denoising

Simple and complex cells revisited: toward a selectivity-invariance model of object recognition.

Autoren: Xin Li Shuo Wang

Quelle: Frontiers in Computational Neuroscience; 2023, p1-13, 13p

Schlagwörter: ARTIFICIAL neural networks, OBJECT recognition (Computer vision), TEMPORAL lobe, TOPOLOGICAL spaces

Learning-based light field imaging: an overview.

Autoren: Mahmoudpour, Saeed Pagliari, Carla Schelkens, Peter

Quelle: EURASIP Journal on Image & Video Processing; 5/30/2024, Vol. 2024 Issue 1, p1-36, 36p

Schlagwörter: VISUAL fields, MACHINE learning, IMAGE processing, COMPUTATIONAL complexity, IMAGE reconstruction