Results 41 to 50 of about 28,961 (286)
Real Color Image Denoising Using t-Product- Based Weighted Tensor Nuclear Norm Minimization
IEEE Access, 2019 Color images can be seen as third-order tensors with column, row and color modes. Considering two inherent characteristics of a color image including the non-local self-similarity (NSS) and the cross-channel correlation, we extract non-local similar ...
Min Liu, Xinggan Zhang, Lan Tang
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Tensor Rank Estimation and Completion via CP-based Nuclear Norm [PDF]
, 2017 Tensor completion (TC) is a challenging problem of recovering missing entries of a tensor from its partial observation. One main TC approach is based on CP/Tucker decomposition.
Cheung, Y.M., Lu, H., Q, S.
core +1 more source
A Hybrid Norm for Guaranteed Tensor Recovery
Frontiers in Physics, 2022 Benefiting from the superiority of tensor Singular Value Decomposition (t-SVD) in excavating low-rankness in the spectral domain over other tensor decompositions (like Tucker decomposition), t-SVD-based tensor learning has shown promising performance and
Yihao Luo +5 more
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Convex Recovery of Tensors Using Nuclear Norm Penalization [PDF]
, 2015 The subdifferential of convex functions of the singular spectrum of real matrices has been widely studied in matrix analysis, optimization and automatic control theory. Convex analysis and optimization over spaces of tensors is now gaining much interest due to its potential applications to signal processing, statistics and engineering. The goal of this
Chretien, Stephane, Wei, Tianwen
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IEEE Access, 2018 The robust and efficient detection of infrared small target is a key technique for infrared search and track systems. Several robust principal component analysis (RPCA)-based methods have been developed recently, which have achieved the state-of-the-art ...
Yang Sun +4 more
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Bilinear Ideals in Operator Spaces [PDF]
, 2015 We introduce a concept of bilinear ideal of jointly completely bounded mappings between operator spaces. In particular, we study the bilinear ideals $\mathcal{N}$ of completely nuclear, $\mathcal{I }$ of completely integral, $\mathcal{E}$ of completely ...
Dimant, Verónica +1 more
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Alternating Direction Method of Multipliers for Generalized Low-Rank Tensor Recovery
Algorithms, 2016 Low-Rank Tensor Recovery (LRTR), the higher order generalization of Low-Rank Matrix Recovery (LRMR), is especially suitable for analyzing multi-linear data with gross corruptions, outliers and missing values, and it attracts broad attention in the fields
Jiarong Shi +3 more
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Cirrus Detection Based on Tensor Multi-Mode Expansion Sum Nuclear Norm in Infrared Imagery
IEEE Access, 2020 Infrared small target detection systems are an important part of space infrared imaging satellites. However, small infrared target detection is often affected by cirrus false alarm sources with similar grayscales.
Chunping Yang +3 more
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Hyperspectral Image Denoising Based on Non-local Similarity and Weighted-truncated NuclearNorm [PDF]
Jisuanji kexue, 2021 Due to the interference of instrumental noise,hyperspectral images (HSI) are often corrupted to some extent by Gaussian noise,which will seriously affect the subsequent performance of image processing.Therefore,image denoising has been considered as an ...
ZHENG Jian-wei, HUANG Juan-juan, QIN Meng-jie, XU Hong-hui, LIU Zhi
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Nonconvex Low Tubal Rank Tensor Minimization
IEEE Access, 2019 In the sparse vector recovery problem, the L0-norm can be approximated by a convex function or a nonconvex function to achieve sparse solutions. In the low-rank matrix recovery problem, the nonconvex matrix rank can be replaced by a convex function or a ...
Yaru Su, Xiaohui Wu, Genggeng Liu
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