Suchergebnisse - "convolutional sparse coding"

Prediction of Prognosis for Nonsmall Cell Lung Cancer After Targeted Therapy Based on CT Imaging and Dense Convolutional Sparse Coding

Autoren: Hua-Jun Lu Hai-Feng Ma Dao-Gui Chen et al.

Quelle: IEEE Sensors Journal. 25:26956-26969

From Convolutional Sparse Coding To *-NMF Factorization of Time-Frequency Coefficients

Autoren: Malagnoux, Jean-Baptiste Kowalski, Matthieu

Weitere Verfasser: Malagnoux, Jean-Baptiste Kowalski, Matthieu

Quelle: ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). :5530-5534

Schlagwörter: Convolutional Dictionary Learning, Convolutional Sparse Coding, [INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing, Time-Frequency synthesis, [INFO.INFO-LG] Computer Science [cs]/Machine Learning [cs.LG], Non-negative Matrix Factorization

Dateibeschreibung: application/pdf

Zugangs-URL: https://hal.science/hal-04608586v1/document
https://hal.science/hal-04608586v1
https://doi.org/10.1109/icassp48485.2024.10447466

Convolutional Sparse Coding with Multipath Orthogonal Matching Pursuit

Autoren: Yanis Gomes Charles Truong Jean-Philippe Saut et al.

Quelle: ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). :1-5

Stable and Lightweight Deep Primal-Dual Unrolling for Constrained Image Restoration with Convolutional Sparse Coding

Autoren: Takafumi Ueki Kazuki Naganuma Shunsuke Ono

Quelle: ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). :1-5

Deep Equilibrium Convolutional Sparse Coding for Hyperspectral Image Denoising

Autoren: Jin Ye Jingran Wang Fengchao Xiong et al.

Quelle: IEEE Transactions on Geoscience and Remote Sensing. 63:1-14

Schlagwörter: FOS: Computer and information sciences, Computer Vision and Pattern Recognition (cs.CV), Image and Video Processing (eess.IV), FOS: Electrical engineering, electronic engineering, information engineering, Image and Video Processing, Computer Vision and Pattern Recognition

Zugangs-URL: http://arxiv.org/abs/2508.15553

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

Deep Content-Dependent 3-D Convolutional Sparse Coding for Hyperspectral Image Denoising

Autoren: Haitao Yin Hao Chen

Quelle: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 4125-4138 (2024)

Schlagwörter: separable convolution, Ocean engineering, deep network, Hyperspectral image denoising, QC801-809, Geophysics. Cosmic physics, 0211 other engineering and technologies, 0202 electrical engineering, electronic engineering, information engineering, convolutional sparse coding, deep unfolding, 02 engineering and technology, TC1501-1800, 3-D convolution

Zugangs-URL: https://doaj.org/article/a64d117c91ac47ad85b4282a042798bc

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

Deep Unrolling of Robust PCA and Convolutional Sparse Coding for Stationary Target Localization in Through Wall Radar Imaging

Autoren: Brehier, Hugo Breloy, Arnaud Ren, Chengfang et al.

Weitere Verfasser: Brehier, Hugo Breloy, Arnaud Ren, Chengfang et al.

Quelle: 2024 32nd European Signal Processing Conference (EUSIPCO). :1881-1885

Schlagwörter: Through-wall imaging, [INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing, Unrolled algorithms

Dateibeschreibung: application/pdf

Zugangs-URL: https://hal.science/hal-04707971v1

Convolutional Sparse Coding for Time Series Via a ℓ0 Penalty: An Efficient Algorithm With Statistical Guarantees

Autoren: Truong, Charles Moreau, Thomas

Weitere Verfasser: Truong, Charles Moreau, Thomas

Quelle: Statistical Analysis and Data Mining: An ASA Data Science Journal. 17

Schlagwörter: statistical learning, L0 penalty, compressive statistical learning, 0202 electrical engineering, electronic engineering, information engineering, convolutional sparse coding, 02 engineering and technology, [INFO] Computer Science [cs], 0101 mathematics, signal processing, [MATH.MATH-ST] Mathematics [math]/Statistics [math.ST], 01 natural sciences

Dateibeschreibung: application/pdf

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

Distributed Compression Coding Based on Convolutional Sparse Coding Using Multiple Key Frames

Autoren: Yosuke Higuchi Muhammad Ajmal Muhaimin Bin Mustafa Yoshimitsu Kuroki

Quelle: 2024 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS). :1-5

A Plug-and-Play Structurally Reparameterized Convolutional Sparse Coding Module for Convolutional Neural Network

Autoren: Yu Song Junshan Xie Jingjing Li et al.

Quelle: 2024 5th International Conference on Computer, Big Data and Artificial Intelligence (ICCBD+AI). :397-403

A Hessian-Driven Convolutional Sparse Coding and Depth Awareness Algorithm for 3D Localization of Light Field Microscopy

Autoren: Zhiyuan Deng Yanjuan Zhao Bohan Qu et al.

Quelle: Proceedings of the 2nd International Workshop on Methodologies for Multimedia. :10-18

Deep Spatial Spectral Convolutional Sparse Coding for Spectral CT Image Reconstruction

Autoren: Jin Liu Jingjing Xie Kun Wang et al.

Quelle: 2024 17th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI). :1-6

Bat Echolocation Scan Pattern Reconstruction using Convolutional Sparse Coding

Autoren: Baeyens, Rens Verreycken, Erik Steckel, Jan et al.

Quelle: 2024 IEEE Applied Sensing Conference (APSCON), 22-24 January, 2024, Goa, India

Schlagwörter: 0301 basic medicine, 0303 health sciences, 03 medical and health sciences, Engineering sciences. Technology

Zugangs-URL: https://hdl.handle.net/10067/2049710151162165141

Enhanced Interpretability in Learned Image Compression via Convolutional Sparse Coding

Autoren: Yiwen Tu Wen Tan Youneng Bao et al.

Quelle: 2024 IEEE International Conference on Multimedia and Expo (ICME). :1-6

Coupled Feature Learning Via Structured Convolutional Sparse Coding for Multimodal Image Fusion

Autoren: Veshki, Farshad G. Vorobyov Sergiy, A. et al.

Weitere Verfasser: Veshki, Farshad G. Vorobyov Sergiy, A. et al.

Quelle: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). :2500-2504

Schlagwörter: 03 medical and health sciences, Multimodal image fusion, 0302 clinical medicine, ta213, structured dictionary learning, 0202 electrical engineering, electronic engineering, information engineering, convolutional sparse coding, 02 engineering and technology

Dateibeschreibung: application/pdf

Zugangs-URL: https://aaltodoc.aalto.fi/handle/123456789/117820

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

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

Quelle: Sensing and Imaging. 26

Compositional Factorization of Visual Scenes with Convolutional Sparse Coding and Resonator Networks

Autoren: Kymn, Christopher J. Mazelet, Sonia Ng, Annabel et al.

Quelle: 2024 Neuro Inspired Computational Elements Conference (NICE). :1-9

Schlagwörter: FOS: Computer and information sciences, Computer Vision and Pattern Recognition (cs.CV), Computer Science - Computer Vision and Pattern Recognition, Computer Science - Neural and Evolutionary Computing, Neural and Evolutionary Computing (cs.NE)

Zugangs-URL: http://arxiv.org/abs/2404.19126