Suchergebnisse - Conventional neural network (CNN) Overcomplete Autoencoder application~

Enhancing Border Learning for Better Image Denoising.

Autoren: Ge, Xin Zhu, Yu Qi, Liping et al.

Quelle: Mathematics (2227-7390); Apr2025, Vol. 13 Issue 7, p1119, 30p

Schlagwörter: ARTIFICIAL neural networks, CONVOLUTIONAL neural networks, AUTOENCODERS, IMAGE denoising, LEARNING ability

Application of 1D ResNet for Multivariate Fault Detection on Semiconductor Manufacturing Equipment †.

Autoren: Tchatchoua, Philip Graton, Guillaume Ouladsine, Mustapha et al.

Quelle: Sensors (14248220); Nov2023, Vol. 23 Issue 22, p9099, 19p

Schlagwörter: SEMICONDUCTOR manufacturing, CONVOLUTIONAL neural networks, MANUFACTURING processes, SEMICONDUCTOR wafers, SEMICONDUCTOR industry

Face recognition algorithm using collaborative sparse representation based on CNN features.

Alternate Title: 基于 CNN 特征的协同稀疏表示人脸识别算法. (Chinese)

Autoren: ZHAO, Shilin XU, Chengjun LIU, Changrong

Quelle: Journal of Measurement Science & Instrumentation; Mar2025, Vol. 16 Issue 1, p85-95, 11p

Schlagwörter: CONVOLUTIONAL neural networks, DEEP learning, DATABASES, ENCYCLOPEDIAS & dictionaries, ALGORITHMS

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

Feature extraction of fields of fluid dynamics data using sparse convolutional autoencoder.

Autoren: Obayashi, Wataru Aono, Hikaru Tatsukawa, Tomoaki et al.

Quelle: AIP Advances; Oct2021, Vol. 11 Issue 10, p1-6, 6p

Schlagwörter: FLUID dynamics, FEATURE extraction, PROPER orthogonal decomposition, NAVIER-Stokes equations, CONVOLUTIONAL neural networks, UNSTEADY flow, COMPRESSIBLE flow

Spatial and Spectral Joint Super-Resolution Using Convolutional Neural Network.

Autoren: Mei, Shaohui Jiang, Ruituo Li, Xu et al.

Quelle: IEEE Transactions on Geoscience & Remote Sensing; Jul2020, Vol. 58 Issue 7, p4590-4603, 14p

Schlagwörter: CONVOLUTIONAL neural networks, SPECTRAL imaging

Designing CNNs for Multimodal Image Restoration and Fusion via Unfolding the Method of Multipliers.

Autoren: Marivani, Iman Tsiligianni, Evaggelia Cornelis, Bruno et al.

Quelle: IEEE Transactions on Circuits & Systems for Video Technology; Sep2022, Vol. 32 Issue 9, p5830-5845, 16p

Schlagwörter: IMAGE reconstruction, IMAGE fusion, CONVOLUTIONAL neural networks, DEEP learning

Mapping the BCPNN Learning Rule to a Memristor Model.

Autoren: Wang, Deyu Xu, Jiawei Stathis, Dimitrios et al.

Quelle: Frontiers in Neuroscience; 12/9/2021, Vol. 15, p1-16, 16p

Schlagwörter: CONVOLUTIONAL neural networks, ANALOG circuits, MACHINE learning

Towards scanning electron microscopy image denoising: a state-of-the-art overview, benchmark, taxonomies, and future direction.

Autoren: Rahman, Sheikh Shah Mohammad Motiur Salomon, Michel Dembélé, Sounkalo

Quelle: Machine Vision & Applications; Jul2024, Vol. 35 Issue 4, p1-20, 20p

Unsupervised Deep Embedded Clustering for High-Dimensional Visual Features of Fashion Images.

Autoren: Malhi, Umar Subhan Zhou, Junfeng Yan, Cairong et al.

Quelle: Applied Sciences (2076-3417); Mar2023, Vol. 13 Issue 5, p2828, 18p

Schlagwörter: CONVOLUTIONAL neural networks

CNN-Based Super-Resolution of Hyperspectral Images.

Autoren: Arun, P. V. Buddhiraju, Krishna Mohan Porwal, Alok et al.

Quelle: IEEE Transactions on Geoscience & Remote Sensing; Sep2020, Vol. 58 Issue 9, p6106-6121, 16p

Schlagwörter: HIGH resolution imaging, CONVOLUTIONAL neural networks, IMAGE intensifiers, HYPERCUBES, THRESHOLDING algorithms, COST functions, RANDOM matrices

Hyperspectral Image Denoising Using a 3-D Attention Denoising Network.

Autoren: Shi, Qian Tang, Xiaopei Yang, Taoru et al.

Quelle: IEEE Transactions on Geoscience & Remote Sensing; Dec2021, Vol. 59 Issue 12, p10348-10363, 16p

Schlagwörter: CONVOLUTIONAL neural networks, IMAGE denoising, DEEP learning, HYPERSPECTRAL imaging systems, BRANCHING processes, MARKOV random fields, PARALLEL processing

Infrared and visible image fusion based on global context network.

Autoren: Li, Yonghong Shi, Yu Pu, Xingcheng et al.

Quelle: Journal of Electronic Imaging; Sep2024, Vol. 33 Issue 5, p53016-14, 1p

Schlagwörter: CONVOLUTIONAL neural networks, INFRARED technology, INFRARED imaging, TRANSFORMER models, HEAT radiation & absorption, DEEP learning, IMAGE fusion

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

Autoren: Yuan N Wang L Ye C et al.

Quelle: Medical physics [Med Phys] 2023 Oct; Vol. 50 (10), pp. 6137-6150. Date of Electronic Publication: 2023 Apr 17.

Publikationsart: Journal Article

Info zur Zeitschrift: Publisher: John Wiley and Sons, Inc Country of Publication: United States NLM ID: 0425746 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2473-4209 (Electronic) Linking ISSN: 00942405 NLM ISO Abbreviation: Med Phys Subsets: MEDLINE

MeSH-Schlagworte: Diffusion Tensor Imaging* , Neural Networks, Computer*, Humans ; Algorithms ; Signal-To-Noise Ratio ; Heart/diagnostic imaging ; Image Processing, Computer-Assisted/methods

Standard Plane Localization in Fetal Ultrasound via Domain Transferred Deep Neural Networks.

Autoren: Chen, Hao Ni, Dong Qin, Jing et al.

Quelle: IEEE Journal of Biomedical & Health Informatics; Sep2015, Vol. 19 Issue 5, p1627-1636, 10p

Schlagwörter: FETAL ultrasonic imaging, ULTRASONICS in obstetrics, NEURAL circuitry, ELECTROPHYSIOLOGY, NERVOUS system

Driving posture recognition by convolutional neural networks.

Autoren: Chao Yan Frans Coenen Bailing Zhang

Quelle: IET Computer Vision (Wiley-Blackwell); 2016, Vol. 10 Issue 2, p103-114, 12p

Schlagwörter: AUTOMOBILE driving, POSTURE, HUMAN body, TRAFFIC accidents, NEURAL circuitry

Hyperspectral image denoising via self-modulated cross-attention deformable convolutional neural network.

Autoren: Wang, Ying Qiu, Jie Zhao, Yanxiang

Quelle: Journal of Electronic Imaging; Jul/Aug2024, Vol. 33 Issue 4, p43015-22, 1p

Schlagwörter: CONVOLUTIONAL neural networks, IMAGE denoising, REMOTE sensing, LIGHT intensity, COMPARATIVE method

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

An Untrained Neural Network Prior for Light Field Compression.

Autoren: Jiang, Xiaoran Shi, Jinglei Guillemot, Christine

Quelle: IEEE Transactions on Image Processing; 2022, Vol. 31, p6922-6936, 15p

Transferred CNN Based on Tensor for Hyperspectral Anomaly Detection.

Autoren: Zhang, Lili Cheng, Baozhi

Quelle: IEEE Geoscience & Remote Sensing Letters; Dec2020, Vol. 17 Issue 12, p2115-2119, 5p