Results 11 to 20 of about 1,797 (255)
Weighted Tensor Nuclear Norm Minimization for Color Image Restoration [PDF]
IEEE Access, 2019 Non-local self-similarity (NLSS) is widely used as prior information in an image restoration method. In particular, a low-rankness-based prior has a significant effect on performance.
Kaito Hosono +2 more
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IEEE Access, 2022 This paper provides a subspace method for closed-loop identification, which clearly specifies the model order from noisy measurement data. The method can handle long I/O data of the target system to be noise-tolerant and determine the model order via ...
Ichiro Maruta, Toshiharu Sugie
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INVERSE HALFTONING BASED ON WEIGHTED NUCLEAR NORM MINIMIZATION [PDF]
Journal of Mechanics in Medicine and Biology, 2021 The inverse halftoning technology refers to restoring a continuous-toned image from a halftoned image with only bi-level pixels. However, recovering the continuous images from their halftoned ones is normally ill-posed, which makes the inverse halftoning algorithm very challenging.
Jun Yang +4 more
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Weighted Nuclear Norm Minimization on Multimodality Clustering
Security and Communication Networks, 2021 Generally, multimodality data contain different potential information available and are capable of providing an enhanced analytical result compared to monosource data. The way to combine the data plays a crucial role in multimodality data analysis which is worth investigating.
Lei Du +4 more
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On Tensor Completion via Nuclear Norm Minimization [PDF]
Foundations of Computational Mathematics, 2015 Many problems can be formulated as recovering a low-rank tensor. Although an increasingly common task, tensor recovery remains a challenging problem because of the delicacy associated with the decomposition of higher order tensors. To overcome these difficulties, existing approaches often proceed by unfolding tensors into matrices and then apply ...
Ming Yuan 0001, Cun-Hui Zhang
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Weighted Schatten p-Norm Low Rank Error Constraint for Image Denoising
Entropy, 2021 Traditional image denoising algorithms obtain prior information from noisy images that are directly based on low rank matrix restoration, which pays little attention to the nonlocal self-similarity errors between clear images and noisy images. This paper
Jiucheng Xu, Yihao Cheng, Yuanyuan Ma
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Weighted t-Schatten-p Norm Minimization for Real Color Image Denoising
IEEE Access, 2020 In this paper, to fully exploit the spatial and spectral correlation information, we present a new real color image denoising scheme using tensor Schatten-p norm (t-Schatten-p norm) minimization based on t-SVD to recover the underlying low-rank tensor ...
Min Liu, Xinggan Zhang, Lan Tang
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A Comparative Study for the Nuclear Norms Minimization Methods [PDF]
2019 IEEE International Conference on Image Processing (ICIP), 2019 The nuclear norm minimization (NNM) is commonly used to approximate the matrix rank by shrinking all singular values equally. However, the singular values have clear physical meanings in many practical problems, and NNM may not be able to faithfully approximate the matrix rank.
Zhiyuan Zha +4 more
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A parallel multi‐block alternating direction method of multipliers for tensor completion
IET Image Processing, 2021 This paper proposes an algorithm for the tensor completion problem of estimating multi‐linear data under the limitation of observation rate. Many tensor completion methods are based on nuclear norm minimization, they may fail to achieve the global ...
Hu Zhu +5 more
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An iterative thresholding-based algorithm for brain–computer interface (BCI) application
Scientific African, 2023 The EEG signals are recorded using surgical (invasive) or non-surgical (non-invasive) BCI neuroimaging modalities. Basically, we are dealing with a huge amount of data and long signal recordings.The classic solution to deal with the huge amount of data ...
Djerassembe Laouhingamaye Frédéric +3 more
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