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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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On the Nuclear Norm and the Singular Value Decomposition of Tensors [PDF]
Foundations of Computational Mathematics, 2015 Finding the rank of a tensor is a problem that has many applications. Unfortunately it is often very difficult to determine the rank of a given tensor. Inspired by the heuristics of convex relaxation, we consider the nuclear norm instead of the rank of a tensor. We determine the nuclear norm of various tensors of interest.
openaire +3 more sources
A Non-Local Tensor Completion Algorithm Based on Weighted Tensor Nuclear Norm
, 2022 In this paper, we proposed an image inpainting algorithm, including an interpolation step and a non-local tensor completion step based on a weighted tensor nuclear norm.
Jingjing Zheng +4 more
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A Generalized Low-Rank Double-Tensor Nuclear Norm Completion Framework for Infrared Small Target Detection [PDF]
, 2022 Infrared small target detection is a research hotspot in computer vision technology that plays an important role in infrared early warning systems.
Zhu, Hu +3 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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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.
Yiu-ming Cheung +5 more
core +1 more source
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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An Efficient Tensor Completion Method Via New Latent Nuclear Norm
IEEE Access, 2020 In tensor completion, the latent nuclear norm is commonly used to induce low-rank structure, while substantially failing to capture the global information due to the utilization of unbalanced unfolding schemes.
Jinshi Yu +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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