Suchergebnisse - multilayer convolutional sparse coding*

An Improved Multilayer Convolutional Sparse Coding Model for Fault Feature Extraction of Rolling Element Bearings Under Varying Speed Conditions

Autoren: Guangming Dong

Quelle: IEEE Transactions on Instrumentation and Measurement. 74:1-12

On Interpretability of CNNs for Multimodal Medical Image Segmentation

Autoren: Lazendic, Srdan Janssens, Jens Huang, Shaoguang et al.

Quelle: EUSIPCO 2022, 30th European Signal Processing Conference, Proceedings

Schlagwörter: multimodal data, medical image segmentation, 03 medical and health sciences, Technology and Engineering, 0302 clinical medicine, deep unrolling, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, interpretable CNNs, Multilayer convolutional sparse coding

Dateibeschreibung: application/pdf

Zugangs-URL: http://hdl.handle.net/1854/LU-8763889
http://doi.org/10.23919/EUSIPCO55093.2022.9909776
https://biblio.ugent.be/publication/8763889/file/8763891
https://biblio.ugent.be/publication/8763889

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

Towards understanding residual and dilated dense neural networks via convolutional sparse coding.

Autoren: Zhang, Zhiyang Zhang, Shihua

Quelle: National Science Review. Mar2021, Vol. 8 Issue 3, p1-13. 13p.

Schlagwörter: *CONVOLUTIONAL neural networks, *THRESHOLDING algorithms

Adaptive Forward-Backward Splitting for Multilayer Convolutional Dictionary Learning.

Autoren: Guan-Ju Peng

Quelle: SIAM Journal on Imaging Sciences; 2025, Vol. 18 Issue 1, p631-664, 34p

Schlagwörter: FORWARD-backward algorithm, DATA dictionaries, NONSMOOTH optimization, IMAGE reconstruction, IMAGE representation

Multilayer Convolutional Sparse Modeling: Pursuit and Dictionary Learning.

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

Quelle: IEEE Transactions on Signal Processing. 8/1/2018, Vol. 66 Issue 15, p4090-4104. 15p.

Schlagwörter: *MULTILAYERS, *ARTIFICIAL neural networks, *FACTORIZATION, *MATHEMATICAL bounds, *ENCODING

SureUnet: sparse autorepresentation encoder U-Net for noise artifact suppression in low-dose CT.

Autoren: Liu, Jin Zhang, Tingyu Kang, Yanqin et al.

Quelle: Neural Computing & Applications; Apr2025, Vol. 37 Issue 11, p7561-7573, 13p

Schlagwörter: COMPUTED tomography, IMAGE processing, IONIZING radiation, ARTIFICIAL intelligence, DEEP learning

Deforestation rate estimation using crossbreed multilayer convolutional neural networks.

Autoren: Subhahan, D. Abdus Kumar, C. N. S. Vinoth

Quelle: Multimedia Tools & Applications; Oct2024, Vol. 83 Issue 33, p79453-79479, 27p

Schlagwörter: CONVOLUTIONAL neural networks, SPECKLE interference, SYNTHETIC aperture radar, DEFORESTATION, ENVIRONMENTAL degradation

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

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

Tire defect classification using a deep convolutional sparse-coding network.

Autoren: Zheng, Zhouzhou Shen, Jinyue Shao, Yuyi et al.

Quelle: Measurement Science & Technology; May2021, Vol. 32 Issue 5, p1-11, 11p

Schlagwörter: CONVOLUTIONAL neural networks, TRAFFIC safety, CONSTRUCTION defects (Buildings), CLASSIFICATION algorithms, FEATURE extraction

PSCSC-Net: A Deep Coupled Convolutional Sparse Coding Network for Pansharpening.

Quelle: IEEE Transactions on Geoscience & Remote Sensing; Jan2022, Vol. 60 Issue 1, p1-16, 16p

Schlagwörter: LINEAR network coding, THRESHOLDING algorithms, DEEP learning, MATHEMATICAL optimization

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

PD-GLCST: integrating graph learning and sparse attention for accurate Parkinson's disease diagnosis.

Autoren: Shermy, R. P. Santhi, S.

Quelle: International Journal of Machine Learning & Cybernetics; Oct2025, Vol. 16 Issue 10, p7637-7658, 22p

Deep High-Order Tensor Convolutional Sparse Coding for Hyperspectral Image Classification.

Autoren: Cheng, Chunbo Li, Hong Peng, Jiangtao et al.

Quelle: IEEE Transactions on Geoscience & Remote Sensing; Mar2022, Vol. 60, p1-11, 11p

Schlagwörter: FEATURE extraction, IMAGE compression, DEEP learning, CLASSIFICATION

Recent advances via convolutional sparse representation model for pixel-level image fusion.

Autoren: Pan, Yue Lan, Tianye Xu, Chongyang et al.

Quelle: Multimedia Tools & Applications; May2024, Vol. 83 Issue 17, p52899-52930, 32p

Schlagwörter: IMAGE fusion, PIXELS, REFERENCE sources, RESEARCH personnel, SOCIAL responsibility of business

Learning the sparse prior: Modern approaches.

Autoren: Peng, Guan‐Ju

Quelle: WIREs: Computational Statistics; Jan/Feb2024, Vol. 16 Issue 1, p1-29, 29p

Schlagwörter: RECURRENT neural networks, SUPERVISED learning, ENCYCLOPEDIAS & dictionaries, PRIOR learning, DATA science

Edge-assisted Object Segmentation Using Multimodal Feature Aggregation and Learning.

Autoren: JIANBO LI GENJI YUAN ZHENG YANG

Quelle: ACM Transactions on Sensor Networks; Jan2024, Vol. 20 Issue 1, p1-22, 22p

Schlagwörter: IMAGE fusion

4 Sparse Coding for Visual Recognition: 4.2 The botany of sparse feature coding.

Autoren: Mairal, Julien Bach, Francis Ponce, Jean

Quelle: Foundations & Trends in Computer Graphics & Vision; 2012, Vol. 8 Issue 2/3, p199-205, 7p

Schlagwörter: VISUAL perception, SPARSE approximations, CODING theory, MACHINE theory, COMPUTER graphics

On Multi-Layer Basis Pursuit, Efficient Algorithms and Convolutional Neural Networks.

Autoren: Sulam, Jeremias Aberdam, Aviad Beck, Amir et al.

Quelle: IEEE Transactions on Pattern Analysis & Machine Intelligence; Aug2020, Vol. 42 Issue 8, p1968-1980, 13p

Schlagwörter: CONVOLUTIONAL neural networks, RECURRENT neural networks, THRESHOLDING algorithms, SUPERVISED learning, ALGORITHMS