3D shape co-segmentation via sparse and low rank representations
Sign Up to like & getrecommendations! Published in 2017 at "Science China Information Sciences"
DOI: 10.1007/s11432-017-9331-9
Abstract: In this paper, we propose a 3D shape co-segmentation method, which divides 3D shapes in the same category into consistent feature representations. We involve sparse and low-rank constraints to obtain compact feature representations among the… read more here.
Keywords: sparse low; segmentation; low rank; shape segmentation ... See more keywords
Bayesian mmWave Channel Estimation via Exploiting Joint Sparse and Low-Rank Structures
Sign Up to like & getrecommendations! Published in 2019 at "IEEE Access"
DOI: 10.1109/access.2019.2910088
Abstract: We consider the problem of channel estimation for millimeter wave (mmWave) systems, where both the base station and the mobile station employ a single radio frequency (RF) chain to reduce the hardware cost and power… read more here.
Keywords: joint sparse; low rank; channel estimation; estimation ... See more keywords
SAR Imaging and Despeckling Based on Sparse, Low-Rank, and Deep CNN Priors
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Geoscience and Remote Sensing Letters"
DOI: 10.1109/lgrs.2021.3131201
Abstract: Synthetic aperture radar (SAR) generally suffers from enormous strains from large quantities of sampling data and serious interferences from the speckle noise. This letter proposes a novel deep network to address these problems. By utilizing… read more here.
Keywords: sparse low; imaging despeckling; low rank; sar imaging ... See more keywords
Fusion of Hyperspectral and LiDAR Data Using Sparse and Low-Rank Component Analysis
Sign Up to like & getrecommendations! Published in 2017 at "IEEE Transactions on Geoscience and Remote Sensing"
DOI: 10.1109/tgrs.2017.2726901
Abstract: The availability of diverse data captured over the same region makes it possible to develop multisensor data fusion techniques to further improve the discrimination ability of classifiers. In this paper, a new sparse and low-rank… read more here.
Keywords: sparse low; fusion hyperspectral; fusion; low rank ... See more keywords
Seismic Data Interpolation Based on Simultaneously Sparse and Low-Rank Matrix Recovery
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Geoscience and Remote Sensing"
DOI: 10.1109/tgrs.2021.3110600
Abstract: Seismic data interpolation is a highly ill-posed problem. Therefore, designing an appropriate regulating method, aiming to reduce multi-solutions, is of utmost importance. Sparse and low-rank priors or constraints, which consider certain kinds of redundant data… read more here.
Keywords: low rank; sparse low; data interpolation; seismic data ... See more keywords
Hyperspectral Image Denoising: Reconciling Sparse and Low-Tensor-Ring-Rank Priors in the Transformed Domain
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Transactions on Geoscience and Remote Sensing"
DOI: 10.1109/tgrs.2023.3237865
Abstract: Recently, the transform-based tensor nuclear norm (TNN) framework has yielded promising results for hyperspectral image (HSI) denoising as compared with previous original-domain tensor-based models. However, the TNN framework only exploits the low-rankness of each band… read more here.
Keywords: tensor ring; tensor; low tensor; sparse low ... See more keywords
Sparse Low-Rank Matrix Estimation With Nonconvex Enhancement for Fault Diagnosis of Rolling Bearings
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Transactions on Instrumentation and Measurement"
DOI: 10.1109/tim.2023.3269103
Abstract: The diagnosis of bearing early fault is significant and fundamental in machine condition monitoring. An accurate and effective diagnosis is of great importance to avoid further serious accidents. However, existing sparse low-rank (SLR) methods for… read more here.
Keywords: fault diagnosis; sparse low; fault; low rank ... See more keywords
Simultaneously Sparse and Low-Rank Matrix Reconstruction via Nonconvex and Nonseparable Regularization
Sign Up to like & getrecommendations! Published in 2018 at "IEEE Transactions on Signal Processing"
DOI: 10.1109/tsp.2018.2867995
Abstract: Many real-world problems involve the recovery of a matrix from linear measurements, where the matrix lies close to some low-dimensional structure. This paper considers the problem of reconstructing a matrix with a simultaneously sparse and… read more here.
Keywords: sparse low; simultaneously sparse; matrix; rank matrix ... See more keywords
A New Model for Sparse and Low-Rank Matrix Decomposition
Sign Up to like & getrecommendations! Published in 2017 at "Journal of Applied Analysis and Computation"
DOI: 10.11948/2017037
Abstract: The robust principal component analysis (RPCA) model is a popular method for solving problems with the nuclear norm and `1 norm. However, it is time-consuming since in general one has to use the singular value… read more here.
Keywords: sparse low; model; rank matrix; low rank ... See more keywords
Multifocus Image Fusion Using a Sparse and Low-Rank Matrix Decomposition for Aviator’s Night Vision Goggle
Sign Up to like & getrecommendations! Published in 2020 at "Applied Sciences"
DOI: 10.3390/app10062178
Abstract: This study proposed the concept of sparse and low-rank matrix decomposition to address the need for aviator’s night vision goggles (NVG) automated inspection processes when inspecting equipment availability. First, the automation requirements include machinery and… read more here.
Keywords: sparse low; image; rank matrix; low rank ... See more keywords