Výsledky vyhľadávania - coupled convolutional sparse coding regularization*

Regularization Parameter-Free Convolutional Sparse Coding via Projections Onto The ℓ1-Ball and The Discrepancy Principle

Autori: Paul Rodriguez

Zdroj: 2018 IEEE 28th International Workshop on Machine Learning for Signal Processing (MLSP). :1-6

Predmety: 0211 other engineering and technologies, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

Prístupová URL adresa: https://dblp.uni-trier.de/db/conf/mlsp/mlsp2018.html#Rodriguez18

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

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

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

Interpretable Multi-Modal Image Registration Network Based on Disentangled Convolutional Sparse Coding.

Autori: Deng, Xin Liu, Enpeng Li, Shengxi a ďalší

Zdroj: IEEE Transactions on Image Processing; 2023, Vol. 32, p1078-1091, 14p

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

Autori: Marivani, Iman Tsiligianni, Evaggelia Cornelis, Bruno a ďalší

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

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

Multi-focus image fusion via online convolutional sparse coding.

Autori: Zhang, Chengfang Zhang, Ziyou Li, Haoyue a ďalší

Zdroj: Multimedia Tools & Applications; Feb2024, Vol. 83 Issue 6, p17327-17356, 30p

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

Autori: Zhang, Han Hua, Jiadong Lin, Jing a ďalší

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

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

Image fusion using online convolutional sparse coding.

Autori: Zhang, Chengfang Zhang, Ziyou Feng, Ziliang

Zdroj: Journal of Ambient Intelligence & Humanized Computing; Oct2023, Vol. 14 Issue 10, p13559-13570, 12p

Structure–texture image decomposition via non-convex total generalized variation and convolutional sparse coding.

Autori: Wang, Chunxue Xu, Linlin Liu, Ligang

Zdroj: Visual Computer; Mar2023, Vol. 39 Issue 3, p1121-1136, 16p

Predmety: HIGH dynamic range imaging, IMAGE processing

Joint bayesian convolutional sparse coding for image super-resolution.

Autori: Ge, Qi Shao, Wenze Wang, Liqian

Zdroj: PLoS ONE; 9/5/2018, Vol. 13 Issue 9, p1-11, 11p

Predmety: HIGH resolution imaging, ALGORITHMS, IMAGE analysis, BAYESIAN analysis, PHYSICAL sciences

A model-guided deep convolutional sparse coding network for hyperspectral and multispectral image fusion.

Autori: Khader, Abdolraheem Xiao, Liang Yang, Jingxiang

Zdroj: International Journal of Remote Sensing; Mar2022, Vol. 43 Issue 6, p2268-2295, 28p

Predmety: MULTISPECTRAL imaging, IMAGE fusion, DEEP learning, LINEAR network coding, THRESHOLDING algorithms, HIGH resolution imaging, SYLVESTER matrix equations

Korporácia: HARVARD University

Pansharpening of Satellite Images with Convolutional Sparse Coding and Adaptive PCNN-Based Approach.

Autori: Sangani, Dhara J. Thakker, Rajesh A. Panchal, S. D. a ďalší

Zdroj: Journal of the Indian Society of Remote Sensing; Dec2021, Vol. 49 Issue 12, p2989-3004, 16p

Convolutional Sparse Autoencoders for Image Classification.

Autori: Luo, Wei Li, Jun Yang, Jian a ďalší

Zdroj: IEEE Transactions on Neural Networks & Learning Systems; Jul2018, Vol. 29 Issue 7, p3289-3294, 6p

Predmety: CODING theory, ARTIFICIAL neural networks, COMBINATORIAL optimization

A Deep Unfolding Network for Multispectral and Hyperspectral Image Fusion.

Autori: Zhang, Bihui Cao, Xiangyong Meng, Deyu

Zdroj: Remote Sensing; Nov2024, Vol. 16 Issue 21, p3979, 18p

Predmety: CONVOLUTIONAL neural networks, IMAGE fusion, DEEP learning, ALGORITHMS, MULTISPECTRAL imaging

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

Autori: JIANBO LI GENJI YUAN ZHENG YANG

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

Predmety: IMAGE fusion

Multimodal Deep Unfolding for Guided Image Super-Resolution.

Autori: Marivani, Iman Tsiligianni, Evaggelia Cornelis, Bruno a ďalší

Zdroj: IEEE Transactions on Image Processing; 2020, Vol. 29, p8443-8456, 14p

Predmety: DEEP learning, HIGH resolution imaging, MULTISPECTRAL imaging, IMAGE reconstruction, INVERSE problems, CONVOLUTIONAL neural networks

Convolutional Sparse Coding for Image Super-Resolution.

Autori: Gu, Shuhang Zuo, Wangmeng Xie, Qi a ďalší

Zdroj: 2015 IEEE International Conference on Computer Vision (ICCV); 1/1/2015, p1823-1831, 9p

Multilayer Convolutional Sparse Modeling: Pursuit and Dictionary Learning.

Autori: Sulam, Jeremias Papyan, Vardan Romano, Yaniv a ďalší

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

Predmety: MULTILAYERS, ARTIFICIAL neural networks, FACTORIZATION, MATHEMATICAL bounds, ENCODING

基于深度展开和双流网络的高光谱图像融合. (Chinese)

Alternate Title: Hyperspectral Image Fusion via Deep Unfolding and Dual‑stream Networks. (English)

Autori: 刘丛 姚佳浩

Zdroj: Journal of Data Acquisition & Processing / Shu Ju Cai Ji Yu Chu Li; Nov2023, Vol. 38 Issue 6, p1406-1421, 16p

Manifold Gradient Descent Solves Multi-Channel Sparse Blind Deconvolution Provably and Efficiently.

Autori: Shi, Laixi Chi, Yuejie

Zdroj: IEEE Transactions on Information Theory; Jul2021, Vol. 67 Issue 7, p4784-4811, 28p

Predmety: INVERSE problems, DECONVOLUTION (Mathematics), SIGNAL processing, COMPUTER vision, SPARSE matrices, DATA modeling

Semi-Coupled Convolutional Sparse Learning for Image Super-Resolution.

Autori: Li, Lingling Zhang, Sibo Jiao, Licheng a ďalší

Zdroj: Remote Sensing; Nov2019, Vol. 11 Issue 21, p2593, 1p

Predmety: HIGH resolution imaging, LINEAR operators, SUPERVISED learning, IMAGE reconstruction algorithms, MATHEMATICAL convolutions, PIXELS