Results 41 to 50 of about 1,415,945 (160)

Sparse and Low-Rank Constrained Tensor Factorization for Hyperspectral Image Unmixing

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021
Third-order tensors have been widely used in hyperspectral remote sensing because of their ability to maintain the 3-D structure of hyperspectral images.
Pan Zheng, Hongjun Su, Qian Du
doaj   +1 more source

Robust Recovery of Corrupted Image Data Based on $L_{1-2}$ Metric

IEEE Access, 2018
For removing noises and recovering intrinsic structure from corrupted image data, a classic modeling approach is based on sparsity assumption. In traditionally, the sparsity is measured by L1-norm.
Fanlong Zhang, Guowei Yang, Zhangjing Yang, Minghua Wan   +3 more
doaj   +1 more source

Tomographic SAR Imaging Method Based on Sparse and Low-rank Structures

Leida xuebao, 2022
This paper proposes a three-dimensional tomographic SAR imaging method based on a combined sparse and low-rank structures. The traditional Compressed Sensing (CS) based tomographic SAR imaging methods only utilize the sparse representation and ...
Yao ZHAO, Juncong XU, Xiangyin QUAN, Li CUI, Zhe ZHANG   +4 more
doaj   +1 more source

Sparse linear representation [PDF]

2009 IEEE International Symposium on Information Theory, 2009
5 pages, to appear in proc.
Jeong, Halyun, Kim, Young-Han
openaire   +2 more sources

Sparse choice models [PDF]

2012 46th Annual Conference on Information Sciences and Systems (CISS), 2012
Choice models, which capture popular preferences over objects of interest, play a key role in making decisions whose eventual outcome is impacted by human choice behavior. In most scenarios, the choice model, which can effectively be viewed as a distribution over permutations, must be learned from observed data.
Farias, Vivek F., Jagabathula, Srikanth, Shah, Devavrat   +2 more
openaire   +4 more sources

PCGen: A Fully Parallelizable Point Cloud Generative Model

Sensors
Generative models have the potential to revolutionize 3D extended reality. A primary obstacle is that augmented and virtual reality need real-time computing.
Nicolas Vercheval, Remco Royen, Adrian Munteanu, Aleksandra Pižurica   +3 more
doaj   +1 more source

A survey of sparse representation: algorithms and applications

, 2015
Sparse representation has attracted much attention from researchers in fields of signal processing, image processing, computer vision and pattern recognition.
Li, Xuelong, Xu, Yong, Yang, Jian, Zhang, David, Zhang, Zheng   +4 more
core   +3 more sources

Hyperspectral Anomaly Detection via Sparse Dictionary Learning Method of Capped Norm

IEEE Access, 2019
Hyperspectral anomaly detection is a research hot spot in the field of remote sensing. It can distinguish abnormal targets from the scene just by utilizing the spectral differences and requiring no prior information.
Yuan Yuan, Dandan Ma, Qi Wang
doaj   +1 more source

Multilevel Quasi-Interpolation on Chebyshev Sparse Grids

Computation
This paper investigates the potential of utilising multilevel quasi-interpolation techniques on Chebyshev sparse grids for complex numerical computations.
Faisal Alsharif
doaj   +1 more source

A Novel Design of Sparse Prototype Filter for Nearly Perfect Reconstruction Cosine-Modulated Filter Banks

Algorithms, 2018
Cosine-modulated filter banks play a major role in digital signal processing. Sparse FIR filter banks have lower implementation complexity than full filter banks, while keeping a good performance level.
Wei Xu, Yi Li, Jinghong Miao, Jiaxiang Zhao, Xin Gao   +4 more
doaj   +1 more source