Suchergebnisse - denoising autoencoder optical images

Research on Reconstruction Method of Occluded Micro-Expressions Based on Quantized Denoising Autoencoder

Autoren: LIU Hui, GUO Te, LIU Dong, LI Yingying

Quelle: Jisuanji gongcheng, Vol 51, Iss 5, Pp 288-304 (2025)

Schlagwörter: occluded micro-expression recognition, feature reconstruction, optical flow, dynamic image, denoising autoencoder, Computer engineering. Computer hardware, TK7885-7895, Computer software, QA76.75-76.765

Dateibeschreibung: electronic resource

Relation: https://www.ecice06.com/fileup/1000-3428/PDF/20250529.pdf; https://doaj.org/toc/1000-3428

Zugangs-URL: https://doaj.org/article/6dca6ccf5e1149279b8fd4d79d81dcd6

Adversarial Defense for Medical Images.

Autoren: Tsai, Min-Jen Lee, Ya-Chu Lien, Hsin-Ying et al.

Quelle: Electronics (2079-9292); Nov2025, Vol. 14 Issue 22, p4384, 46p

Schlagwörter: DEEP learning, AUTOENCODERS, ARTIFICIAL intelligence, MEDICAL photography, SIGNAL denoising, IMAGE quality in imaging systems

Denoising Letter Images from Scanned Invoices Using Stacked Autoencoders.

Autoren: Alshathri, Samah Ibrahim Vincent, Desiree Juby Hari, V. S.

Quelle: Computers, Materials & Continua; 2021, Vol. 71 Issue 1, p1371-1386, 16p

Schlagwörter: IMAGE denoising, SIGNAL-to-noise ratio, OPTICAL character recognition, DEEP learning, INVOICES

Semantic denoising autoencoders for retinal optical coherence tomography

Autoren: Boppart, Stephen A. Wojtkowski, Maciej Oh, Wang-Yuhl et al.

Index Begriffe: Computer-aided diagnosis, Image classication, Image enhancement, Machine learning, BookPart, Text

URL: https://www.repo.uni-hannover.de/handle/123456789/10347
https://doi.org/10.1117/12.2526936
Optical Coherence Imaging Techniques and Imaging in Scattering Media III : 23-27 June 2019, Munich, Germany
Proceedings of SPIE 11078 (2019)
1996-756X
978-1-5106-2850-2
978-1-5106-2849-6
0277-786X
https://doi.org/10.1117/12.2526936

Lightweight CNN-based eye diagrams denoising autoencoder for 16-QAM IsOWC system.

Autoren: Suman, Subhash Raut, Subhradeep Mishra, Jitendra K.

Quelle: Optical Engineering; Oct2025, Vol. 64 Issue 10, p108103-108103, 1p

Advancing Self-Supervised Learning for Building Change Detection and Damage Assessment: Unified Denoising Autoencoder and Contrastive Learning Framework.

Autoren: Yang, Songxi Peng, Bo Sui, Tang et al.

Quelle: Remote Sensing; Aug2025, Vol. 7 Issue 15, p2717, 28p

Schlagwörter: FEATURE extraction, AUTOENCODERS, STRUCTURAL health monitoring, URBAN research, EMERGENCY management

Denoising-autoencoder-facilitated MEMS computational spectrometer with enhanced resolution on a silicon photonic chip.

Autoren: Zhou, Jing Zhang, Hui Qiao, Qifeng et al.

Quelle: Nature Communications; 11/26/2024, Vol. 15 Issue 1, p1-12, 12p

Schlagwörter: SIGNAL-to-noise ratio, SPECTROMETERS, PHOTONICS, SILICON, INTERFEROMETERS, IMAGE denoising

A Hierarchical Low-Rank Denoising Model for Remote Sensing Images Based on Deep Unfolding.

Autoren: Shao, Fanqi Feng, Xiaolin Tian, Sirui et al.

Quelle: Sensors (14248220); Jul2024, Vol. 24 Issue 14, p4574, 22p

Schlagwörter: IMAGE denoising, REMOTE sensing, ARTIFICIAL neural networks, SYNTHETIC aperture radar, OPTICAL images

Point spread function modelling for wide-field small-aperture telescopes with a denoising autoencoder

Autoren: Zhengyang Li Xiyu Li Peng Jia et al.

Quelle: Monthly notices of the Royal Astronomical Society, 2020, Vol.493(1), pp.651-660 [Peer Reviewed Journal]

Schlagwörter: FOS: Computer and information sciences, Computer Science - Machine Learning, Image and Video Processing (eess.IV), 0103 physical sciences, FOS: Electrical engineering, electronic engineering, information engineering, FOS: Physical sciences, Electrical Engineering and Systems Science - Image and Video Processing, Astrophysics - Instrumentation and Methods for Astrophysics, Instrumentation and Methods for Astrophysics (astro-ph.IM), 01 natural sciences, Machine Learning (cs.LG)

Dateibeschreibung: application/pdf

Zugangs-URL: http://dro.dur.ac.uk/30503/1/30503.pdf
http://arxiv.org/abs/2001.11716
http://export.arxiv.org/pdf/2001.11716
https://academic.oup.com/mnras/article/493/1/651/5739828
https://dro.dur.ac.uk/30503/
http://ui.adsabs.harvard.edu/abs/2020MNRAS.493..651J/abstract
https://arxiv.org/abs/2001.11716v1
https://arxiv.org/pdf/2001.11716.pdf

Offline Urdu Nastaleeq optical character recognition based on stacked denoising autoencoder

Autoren: Xiaojie Wang Ibrar Ahmad Ruifan Li et al.

Quelle: China Communications. 14:146-157

Schlagwörter: 13. Climate action, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

Zugangs-URL: http://ieeexplore.ieee.org/document/7839765/
https://ieeexplore.ieee.org/document/7839765/

The Application of Deep Convolutional Denoising Autoencoder for Optical Character Recognition Preprocessing

Autoren: Iwan Njoto Sandjaja Kartika Gunadi Christopher Wiraatmaja

Quelle: 2017 International Conference on Soft Computing, Intelligent System and Information Technology (ICSIIT). :72-77

Schlagwörter: 0301 basic medicine, 03 medical and health sciences, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

Zugangs-URL: http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=8262546
http://xplorestaging.ieee.org/ielx7/8258710/8262523/08262546.pdf?arnumber=8262546

A Dual-Branch Autoencoder Network for Underwater Low-Light Polarized Image Enhancement.

Autoren: Xue, Chang Liu, Qingyu Huang, Yifan et al.

Quelle: Remote Sensing; Apr2024, Vol. 16 Issue 7, p1134, 24p

Schlagwörter: IMAGE intensifiers, IMAGE enhancement (Imaging systems), FEATURE extraction, OPTICAL images

A Novel Aerosol Optical Depth Retrieval Method Based on SDAE from Himawari-8/AHI Next-Generation Geostationary Satellite in Hubei Province.

Autoren: Deng, Shiquan Bai, Ting Chen, Zhe et al.

Quelle: Remote Sensing; Apr2025, Vol. 17 Issue 8, p1396, 18p

Schlagwörter: EXTREME learning machines, ATMOSPHERIC aerosols, GEOSTATIONARY satellites, AUTOENCODERS, SUPPORT vector machines

Sequential SAR-to-Optical Image Translation.

Autoren: Wei, Jingbo Zhou, Huan Ke, Peng et al.

Quelle: Remote Sensing; Jul2025, Vol. 17 Issue 13, p2287, 19p

Schlagwörter: SYNTHETIC aperture radar, TRANSFORMER models, FEATURE extraction

Implicit guidance for enhancing low-light optical flow estimation via channel attention networks.

Autoren: Dai, Weichen Wu, Hexing Weng, Xiaoyang et al.

Quelle: Visual Computer; Aug2025, Vol. 41 Issue 10, p7855-7865, 11p

Schlagwörter: OPTICAL flow, FEATURE extraction, OPTICAL images, COMPUTER vision, IMAGE intensifiers, IMAGE enhancement (Imaging systems)

An Image Processing-Based Approach for Reading Needle-Type Instruments on Aircraft.

Autoren: Gümüş, Fatma Eyüpoğlu, Can

Quelle: Electrica; May2024, Vol. 24 Issue 2, p425-435, 11p

Schlagwörter: IMAGE analysis, IMAGE processing, SIGNAL denoising, DASHBOARDS (Management information systems), AIRPLANE cockpits, MODEL airplanes, AUTOENCODERS, LEAST squares

Automatic facial expression recognition under partial occlusion based on motion reconstruction using a denoising autoencoder.

Autoren: Kemmou, Abdelaali El Makrani, Adil El Azami, Ikram et al.

Quelle: Indonesian Journal of Electrical Engineering & Computer Science; Apr2024, Vol. 34 Issue 1, p276-289, 14p

Schlagwörter: FACIAL expression, RESEARCH personnel, ROAD safety measures, MOTION

Signal‐to‐noise ratio enhancement for Raman spectra based on optimized Raman spectrometer and convolutional denoising autoencoder.

Autoren: Fan, Xian‐guang Zeng, Yingjie Zhi, Yu‐Liang et al.

Quelle: Journal of Raman Spectroscopy; Apr2021, Vol. 52 Issue 4, p890-900, 11p

Schlagwörter: RAMAN spectroscopy, SIGNAL-to-noise ratio, IMAGE denoising, RAMAN lasers, SPECTROMETERS

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

Autoren: Han, Ming Dang, Yu Han, Jianda

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

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

Deep Learning-Based Noise Type Classification and Removal for Drone Image Restoration.

Autoren: Ahmed, Waqar Khan, Sajid Noor, Adeeb et al.

Quelle: Arabian Journal for Science & Engineering (Springer Science & Business Media B.V. ); Mar2024, Vol. 49 Issue 3, p4287-4306, 20p

Schlagwörter: IMAGE denoising, IMAGE reconstruction, NOISE, DEEP learning