Suchergebnisse - Multi-layer Convolutional Sparse Coding

A Comprehensive Review of Multi- Layer Convolutional Sparse Coding in Semantic Segmentation

Autoren: Geetanjali Sharma Sumit Kushwaha

Quelle: 2024 9th International Conference on Communication and Electronics Systems (ICCES). :2050-2054

Multi-layer Convolutional Sparse Coding Framework for Restoration of Under-sampled MR Images

Autoren: Abdul Wahab Usman Ullah Abdul Wahid Kushairy Kadir et al.

Quelle: 2021 IEEE International Instrumentation and Measurement Technology Conference (I2MTC). :1-6

Schlagwörter: 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

Zugangs-URL: https://dblp.uni-trier.de/db/conf/i2mtc/i2mtc2021.html#WahidUKS21

CSC-Unet: A Novel Convolutional Sparse Coding Strategy Based Neural Network for Semantic Segmentation

Autoren: Haitong Tang Shuang He Mengduo Yang et al.

Quelle: IEEE Access, Vol 12, Pp 35844-35854 (2024)

Schlagwörter: convolution operation, FOS: Computer and information sciences, Computer Science - Machine Learning, Computer Science - Artificial Intelligence, convolutional sparse coding (CSC), Computer Vision and Pattern Recognition (cs.CV), Computer Science - Computer Vision and Pattern Recognition, deep learning, 02 engineering and technology, U-Net, semantic segmentation, TK1-9971, Machine Learning (cs.LG), 03 medical and health sciences, Artificial Intelligence (cs.AI), 0302 clinical medicine, 0202 electrical engineering, electronic engineering, information engineering, Electrical engineering. Electronics. Nuclear engineering

Zugangs-URL: http://arxiv.org/abs/2108.00408
https://doaj.org/article/e30804863cc34fd3badd7f3f2ed88785

Projecting on to the Multi-Layer Convolutional Sparse Coding Model

Autoren: Jeremias Sulam Vardan Papyant Yaniv Romano et al.

Quelle: 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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

Zugangs-URL: https://dblp.uni-trier.de/db/conf/icassp/icassp2018.html#SulamPRE18
http://dblp.uni-trier.de/db/conf/icassp/icassp2018.html#SulamPRE18
https://ieeexplore.ieee.org/document/8462552/

WSEBP: A Novel Width-depth Synchronous Extension-based Basis Pursuit Algorithm for Multi-Layer Convolutional Sparse Coding

Autoren: Tang, Haitong He, Shuang Bian, Lingbin et al.

Index Begriffe: Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, text

URL: http://arxiv.org/abs/2203.14856

Denoising convolutional neural network inspired via multi-layer convolutional sparse coding

Autoren: Zejia Wen Jianjun Wang Hailin Wang et al.

Quelle: Journal of Electronic Imaging. 30

Schlagwörter: 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

Zugangs-URL: https://dblp.uni-trier.de/db/journals/jei/jei30.html#WenWGW21
https://doi.org/10.1117/1.JEI.30.2.023007
http://ui.adsabs.harvard.edu/abs/2021JEI....30b3007W/abstract
https://www.spiedigitallibrary.org/journals/journal-of-electronic-imaging/volume-30/issue-2/023007/Denoising-convolutional-neural-network-inspired-via-multi-layer-convolutional-sparse/10.1117/1.JEI.30.2.023007.full

一种基于多尺度的多层卷积稀疏编码网络. (Chinese)

Alternate Title: A Multi-layer Convolutional Sparse Coding Network Based on Multi-Scale. (English)

Autoren: 谢伟立 张 军

Quelle: Journal of Guangdong University of Technology; Nov2024, Vol. 41 Issue 6, p125-132, 8p

Schlagwörter: IMAGE recognition (Computer vision), CONVOLUTIONAL neural networks, FEATURE selection, LINEAR network coding, DESIGN techniques

Damage localization with Lamb waves using dense convolutional sparse coding network

Autoren: Han Zhang Jiadong Hua Jing Lin et al.

Quelle: Structural Health Monitoring. 22:1180-1192

Schlagwörter: 0103 physical sciences, 01 natural sciences

Interpretable convolutional sparse coding method of Lamb waves for damage identification and localization

Autoren: Han Zhang Jing Lin Jiadong Hua et al.

Quelle: Structural Health Monitoring. 21:1790-1804

Schlagwörter: 0103 physical sciences, 01 natural sciences

Zugangs-URL: https://journals.sagepub.com/doi/abs/10.1177/14759217211044806

CSC-Unet: A Novel Convolutional Sparse Coding Strategy Based Neural Network for Semantic Segmentation

Autoren: Tang, Haitong He, Shuang Yang, Mengduo et al.

Index Begriffe: Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, Computer Science - Machine Learning, text

URL: http://arxiv.org/abs/2108.00408

Multi-Layer Basis Pursuit for Compressed Sensing MR Image Reconstruction

Autoren: Abdul Wahid Jawad Ali Shah Adnan Umar Khan et al.

Quelle: IEEE Access, Vol 8, Pp 186222-186232 (2020)

Schlagwörter: multi-layered convolutional sparse coding, Signal processing, Radiology, Nuclear Medicine and Imaging, Artificial intelligence, Economics, Computational Mechanics, Biomedical Engineering, Convolutional neural network, 02 engineering and technology, FOS: Medical engineering, Pattern recognition (psychology), Sparse Approximation, 03 medical and health sciences, Engineering, Magnetic resonance imaging, 0302 clinical medicine, Compressed Sensing, Health Sciences, Image (mathematics), 0202 electrical engineering, electronic engineering, information engineering, Orthogonal Matching Pursuit, Basis pursuit, Economic growth, Signal reconstruction, Radar, Deep learning, Advances in Photoacoustic Imaging and Tomography, Compressive sensing, Theory and Applications of Compressed Sensing, iterative thresholding algorithms, Magnetic Resonance Imaging, Computer science, TK1-9971, Iterative reconstruction, inverse problems in imaging, Thresholding, Physical Sciences, Convergence (economics), Matching pursuit, Telecommunications, Medicine, Quantitative Susceptibility Mapping, Compressed sensing, Computer vision, Electrical engineering. Electronics. Nuclear engineering, Radiology, Magnetic Resonance Imaging Applications in Medicine

Zugangs-URL: https://doaj.org/article/0f99a9b6256d4122baa3bc01a9e6bffa
https://dblp.uni-trier.de/db/journals/access/access8.html#WahidSKAR20
https://ieeexplore.ieee.org/document/9214501

Multi-layer convolutional dictionary learning network for signal denoising and its application to explainable rolling bearing fault diagnosis.

Autoren: Qin, Yi Yang, Rui He, Biao et al.

Quelle: ISA Transactions. Apr2024, Vol. 147, p55-70. 16p.

Schlagwörter: Signal denoising, Roller bearings, Fault diagnosis, Artificial neural networks, Thresholding algorithms, Vibration (Mechanics), Deep learning

Damage localization with Lamb waves using dense convolutional sparse coding network.

Autoren: Zhang, Han Hua, Jiadong Lin, Jing et al.

Quelle: Structural Health Monitoring; Mar2023, Vol. 22 Issue 2, p1180-1192, 13p

Schlagwörter: LAMB waves, DEEP learning, LINEAR network coding, MACHINE learning, STRUCTURAL health monitoring, THRESHOLDING algorithms, MATHEMATICAL convolutions

Towards Understanding Residual and Dilated Dense Neural Networks via Convolutional Sparse Coding

Autoren: Zhang, Zhiyang Zhang, Shihua

Index Begriffe: Computer Science - Machine Learning, Computer Science - Computer Vision and Pattern Recognition, Statistics - Machine Learning, text

URL: http://arxiv.org/abs/1912.02605

Multi-Layer Deep Sparse Representation for Biological Slice Image Inpainting.

Autoren: Haitao Hu Hongmei Ma Shuli Mei

Quelle: Computers, Materials & Continua; 2023, Vol. 76 Issue 3, p3813-3832, 20p

Schlagwörter: INPAINTING, CONVOLUTIONAL neural networks, PARTIAL differential equations, CROSS-sectional imaging, IMAGE reconstruction

Interpretable convolutional sparse coding method of Lamb waves for damage identification and localization.

Autoren: Zhang, Han Lin, Jing Hua, Jiadong et al.

Quelle: Structural Health Monitoring; Jul2022, Vol. 21 Issue 4, p1790-1804, 15p

Schlagwörter: LAMB waves, DEEP learning, STRUCTURAL health monitoring, FEATURE extraction, NONDESTRUCTIVE testing, CONVOLUTIONAL neural networks

SUPER-RESOLUTION IMAGE RECONSTRUCTION METHOD BASED ON DEEP CONVOLUTIONAL SPARSE CODING

Multi Attention Convolutional Sparse Coding U-Net for Enhanced Land-Use and Land-Cover Segmentation Using Hyperspectral Images.

Autoren: Yele, Vijaykumar P. Badhe, Namdeo Baban Alegavi, Sujata et al.

Quelle: Sensing & Imaging; 5/20/2025, Vol. 26 Issue 1, p1-32, 32p

Multi-Layered Basis Pursuit Algorithms for Classification of MR Images of Knee ACL Tear

Autoren: Abdul Wahid Jawad Ali Shah Adnan Umar Khan et al.

Quelle: IEEE Access, Vol 8, Pp 205424-205435 (2020)

Schlagwörter: Artificial intelligence, multi-layer convolutional sparse coding, Biomedical Engineering, Convolutional neural network, Projections and Outcomes of Arthroplasty Surgery in the US, 02 engineering and technology, FOS: Medical engineering, Pattern recognition (psychology), Biomechanical Analysis, iterative shrinkage algorithms, Engineering, Health Sciences, Machine learning, Image (mathematics), 0202 electrical engineering, electronic engineering, information engineering, Basis pursuit, knee MR image classification, Deep learning, Advances in Photoacoustic Imaging and Tomography, Computer science, Contextual image classification, TK1-9971, Algorithm, Knee Biomechanics, In Vivo Imaging, Physical Sciences, Medicine, Surgery, Electrical engineering. Electronics. Nuclear engineering, Anterior Cruciate Ligament Injuries in Athletes

Zugangs-URL: https://doaj.org/article/091ac545b21d4301a35bc141ed52c44a
https://dblp.uni-trier.de/db/journals/access/access8.html#WahidSKUA20
https://doi.org/10.1109/ACCESS.2020.3037745
https://ieeexplore.ieee.org/document/9257450/

Multi-Layer Convolutional Sparse Modeling: Pursuit and Dictionary Learning

Autoren: Sulam, Jeremias Papyan, Vardan Romano, Yaniv et al.

Index Begriffe: Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Statistics - Machine Learning, text

URL: http://arxiv.org/abs/1708.08705