Search Results - "deep convolutional autoencoder network"

Spatiotemporal Image-Based Flight Trajectory Clustering Model with Deep Convolutional Autoencoder Network

Authors: Ye Liu Kam K. H. Ng Nana Chu et al.

Source: Journal of Aerospace Information Systems. 20:575-587

Subject Terms: 0502 economics and business, 05 social sciences, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

Inpainting non-anatomical objects in brain imaging using enhanced deep convolutional autoencoder network

Authors: Puranam Revanth Kumar B Shilpa Rajesh Kumar Jha et al.

Source: Sādhanā. 49

Fast Dual Deep Convolutional Autoencoder Network for Denoising Monte Carlo Rendering

Authors: Ahmed Mustafa Taha Alzbier Chunyi Chen Zhongye Shen et al.

Source: Journal of Imaging Science and Technology. 67:030501-1

Inpainting non-anatomical objects in brain imaging using enhanced deep convolutional autoencoder network.

Authors: Kumar, Puranam Revanth Shilpa, B Jha, Rajesh Kumar et al.

Source: Sādhanā: Academy Proceedings in Engineering Sciences; Jun2024, Vol. 49 Issue 2, p1-13, 13p

Fast Dual Deep Convolutional Autoencoder Network for Denoising Monte Carlo Rendering.

Authors: Alzbier, Ahmed Mustafa Taha Chunyi Chen Zhongye Shen et al.

Source: Journal of Imaging Science & Technology; May/Jun2023, Vol. 67 Issue 3, p1-12, 12p

Subject Terms: DEEP learning, CONVOLUTIONAL neural networks, PIXELS, IMAGE reconstruction, SIGNAL-to-noise ratio

Attention mechanism-based deep denoiser for desert seismic random noise suppression.

Authors: Lin, Hongbo Liu, Chang Wang, Shigang et al.

Source: Acta Geophysica. Dec2023, Vol. 71 Issue 6, p2781-2793. 13p.

Subject Terms: *RANDOM noise theory, *MICROSEISMS, *SIGNAL-to-noise ratio, *FEATURE extraction, *SIGNAL denoising, *DESERTS

Single-Pixel Imaging Based on Deep Learning Enhanced Singular Value Decomposition.

Authors: Deng, Youquan She, Rongbin Liu, Wenquan et al.

Source: Sensors (14248220); May2024, Vol. 24 Issue 10, p2963, 14p

Subject Terms: SINGULAR value decomposition, DEEP learning

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

Attention mechanism-based deep denoiser for desert seismic random noise suppression

Authors: Lin, Hongbo Liu, Chang Wang, Shigang et al.

Subject Terms: seismic explorations, random noise suppression, deep convolutional autoencoder network, attention mechanism, low signal-to-noise ratio

Access URL: http://yadda.icm.edu.pl/baztech/element/bwmeta1.element.baztech-6efc455c-5479-4f38-ad63-96fee9d0a7b3

A Depth-Wise Separable U-Net Architecture with Multiscale Filters to Detect Sinkholes.

Authors: Alshawi, Rasha Hoque, Md Tamjidul Flanagin, Maik C.

Source: Remote Sensing; Mar2023, Vol. 15 Issue 5, p1384, 18p

Subject Terms: SINKHOLES, CELL nuclei, DEEP learning

Deep Spatiotemporal Clutter Filtering of Transthoracic Echocardiographic Images: Leveraging Contextual Attention and Residual Learning

Authors: Tabassian, Mahdi Akbari, Somayeh Queirós, Sandro et al.

Index Terms: Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, text

URL: http://arxiv.org/abs/2401.13147

Atmospheric Turbulence Removal in Long-Range Imaging Using a Data-Driven-Based Approach.

Authors: Fazlali, Hamidreza Shirani, Shahram BradforSd, Michael et al.

Source: International Journal of Computer Vision; Apr2022, Vol. 130 Issue 4, p1031-1049, 19p

Subject Terms: ATMOSPHERIC turbulence, K-means clustering, SEARCH algorithms

Spectral-Spatial Deep Support Vector Data Description for Hyperspectral Anomaly Detection.

Authors: Li, Kun Ling, Qiang Qin, Yao et al.

Source: IEEE Transactions on Geoscience & Remote Sensing; Mar2022, Vol. 60, p1-16, 16p

Subject Terms: ANOMALY detection (Computer security), VECTOR data, DEEP learning, LATENT variables

Trajectory Segmentation and Clustering in Terminal Airspace Using Transformer–VAE and Density-Aware Optimization.

Authors: Chen, Quanquan Le, Meilong

Source: Aerospace (MDPI Publishing); Nov2025, Vol. 12 Issue 11, p969, 27p

Subject Terms: CLUSTERING algorithms, TRAFFIC monitoring, MATHEMATICAL optimization, OUTLIER detection, TRAJECTORIES (Mechanics), EMPIRICAL research, MACHINE learning

DeepGhost: real-time computational ghost imaging via deep learning.

Authors: Rizvi, Saad Cao, Jie Zhang, Kaiyu et al.

Source: Scientific Reports; 7/9/2020, Vol. 10 Issue 1, p1-9, 9p

Subject Terms: DEEP learning, BIG data, RECONSTRUCTION (Linguistics), HISTORICAL linguistics, ARCHITECTURE, COMPUTATIONAL acoustics

Hyperspectral unmixing using deep convolutional autoencoder.

Authors: Elkholy, Menna M. Mostafa, Marwa Ebied, Hala M. et al.

Source: International Journal of Remote Sensing; Jun2020, Vol. 41 Issue 12, p4799-4819, 21p, 1 Diagram, 9 Charts, 10 Graphs, 2 Maps

Subject Terms: STANDARD deviations, REMOTE sensing

Single-Pixel Three-Dimensional Compressive Imaging System Using Volume Structured Illumination.

Authors: Jiang, Yanbing Mu, Shaoshuo

Source: Electronics (2079-9292); Sep2025, Vol. 14 Issue 17, p3463, 19p

Subject Terms: THREE-dimensional imaging, COMPRESSED sensing, IMAGING systems, MATHEMATICAL optimization, ANALYSIS of colors

Deep-learned orthogonal basis patterns for fast, noise-robust single-pixel imaging

Authors: Aguilar, Ritz Ann Dailisan, Damian

Index Terms: Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Physics - Instrumentation and Detectors, text

URL: http://arxiv.org/abs/2205.08736

Automated, AI-based Inspection of Drive Wheels on Overhead Hoist Transport Vehicles

Additional Titles: Automatische, KI-basierte Inspektion von Antriebsrädern an Overhead Ho-ist Transportfahrzeugen

Authors: Zhu, Hailong Rank, Sebastian Schmidt, Thorsten

Source: Logistics Journal : Proceedings ; 2021 , 17

Index Terms: 620, http://dewey.info/class/620, Fehlererkennung, OHT, Verschleißmodell, Zustandsüberwachung, autoencoder, condition monitoring, faults detection, wear out model

URL: https://www.logistics-journal.de/proceedings/2021/5450

OutlierNets: Highly Compact Deep Autoencoder Network Architectures for On-Device Acoustic Anomaly Detection

Authors: Abbasi, Saad Famouri, Mahmoud Shafiee, Mohammad Javad et al.

Index Terms: Computer Science - Sound, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Computer Science - Neural and Evolutionary Computing, text

URL: http://arxiv.org/abs/2104.00528