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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A QoS Prediction Approach Based on Truncated Nuclear Norm Low-Rank Tensor Completion
Sensors, 2022 With the rise of mobile edge computing (MEC), mobile services with the same or similar functions are gradually increasing. Usually, Quality of Service (QoS) has become an indicator to measure high-quality services.
Hong Xia +5 more
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Framelet Representation of Tensor Nuclear Norm for Third-Order Tensor Completion [PDF]
IEEE Transactions on Image Processing, 2020 The main aim of this paper is to develop a framelet representation of the tensor nuclear norm for third-order tensor completion. In the literature, the tensor nuclear norm can be computed by using tensor singular value decomposition based on the discrete Fourier transform matrix, and tensor completion can be performed by the minimization of the tensor ...
Tai-Xiang Jiang +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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Low-Rank Tensor Completion for Image and Video Recovery via Capped Nuclear Norm
IEEE Access, 2019 Inspired by the robustness and efficiency of the capped nuclear norm, in this paper, we apply it to 3D tensor applications and propose a novel low-rank tensor completion method via tensor singular value decomposition (t-SVD) for image and video recovery.
Xi Chen +5 more
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Hyperspectral Image Denoising via Framelet Transformation Based Three-Modal Tensor Nuclear Norm
Remote Sensing, 2021 During the acquisition process, hyperspectral images (HSIs) are inevitably contaminated by mixed noise, which seriously affects the image quality. To improve the image quality, HSI denoising is a critical preprocessing step.
Wenfeng Kong, Yangyang Song, Jing Liu
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Low-Rank Tensor Completion via Tensor Nuclear Norm With Hybrid Smooth Regularization
IEEE Access, 2019 As a convex surrogate of tensor multi rank, recently the tensor nuclear norm (TNN) obtains promising results in the tensor completion. However, only considering the low-tubal-rank prior is not enough for recovering the target tensor, especially when the ...
Xi-Le Zhao +4 more
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Robust Tensor Factorization for Color Image and Grayscale Video Recovery
IEEE Access, 2020 Low-rank tensor completion (LRTC) plays an important role in many fields, such as machine learning, computer vision, image processing, and mathematical theory.
Shiqiang Du +4 more
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