Search Results - cnn overcomplete autoencoder (inference OR reference) errors analysis~

Moving forward in water distribution network leak identification through an innovative features engineering step.

Authors: Damonti, Elvio Bernasconi, Giancarlo

Source: Digital Signal Processing. Jan2026:Part C, Vol. 168, pN.PAG-N.PAG. 1p.

Subject Terms: *WATER distribution, *LEAK detection, *FEATURE selection, *PATTERN perception, *AUTOENCODERS, *CONVOLUTIONAL neural networks, *OUTLIER detection

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

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

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

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

Overcomplete graph convolutional denoising autoencoder for noisy skeleton action recognition.

Authors: Guo, Jiajun Ji, Qingge Shan, Guangwei

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

Subject Terms: SIGNAL denoising, COMPUTER vision

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

Authors: S., Saravanan Juliet, Sujitha

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

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

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

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

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

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

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

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

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

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

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

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

Subject Terms: IMAGE fusion, INFRARED photography, COMPUTATIONAL complexity, QUANTITATIVE research, IMAGE processing

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

Authors: Sandhu, Manbir Kushwaha, Sumit Arora, Tanvi

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

ECG Signal Classification of Cardiovascular Disorder using CWT and DCNN.

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

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

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

Efficient Diffusion Posterior Sampling for Noisy Inverse Problems.

Authors: Li, Ji Wang, Chao

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

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

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

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

eXplainable AI for Word Embeddings: A Survey.

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

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

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

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

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

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

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

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

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

Learning Latent Representations of 3D Human Pose with Deep Neural Networks.

Authors: Katircioglu, Isinsu Tekin, Bugra Salzmann, Mathieu et al.

Source: International Journal of Computer Vision; Dec2018, Vol. 126 Issue 12, p1326-1341, 16p, 2 Color Photographs, 7 Diagrams, 10 Charts, 1 Graph

Subject Terms: DEEP learning, ARTIFICIAL neural networks, POSE estimation (Computer vision), COMPUTER vision, IMAGE reconstruction

Learning-based light field imaging: an overview.

Authors: Mahmoudpour, Saeed Pagliari, Carla Schelkens, Peter

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

Subject Terms: VISUAL fields, MACHINE learning, IMAGE processing, COMPUTATIONAL complexity, IMAGE reconstruction

Restoration Guarantee of Image Inpainting via Low Rank Patch Matrix Completion.

Authors: Jian-Feng Cai Jae Kyu Choi Jingyang Li et al.

Source: SIAM Journal on Imaging Sciences; 2024, Vol. 17 Issue 3, p1879-1908, 30p

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

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

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

Deep convolutional autoencoders as generic feature extractors in seismological applications.

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

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

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

OSLO: On-the-Sphere Learning for Omnidirectional Images and Its Application to 360-Degree Image Compression.

Authors: Mahmoudian Bidgoli, Navid de A. Azevedo, Roberto G. Maugey, Thomas et al.

Source: IEEE Transactions on Image Processing; 2022, Vol. 31, p5813-5827, 15p