Results 21 to 30 of about 1,958,095 (294)
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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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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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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Sensors, 2022 Although wireless sensor networks (WSNs) have been widely used, the existence of data loss and corruption caused by poor network conditions, sensor bandwidth, and node failure during transmission greatly affects the credibility of monitoring data.
Jingfei He +3 more
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CoRR, 2017 Rank minimization methods have attracted considerable interest in various areas, such as computer vision and machine learning. The most representative work is nuclear norm minimization (NNM), which can recover the matrix rank exactly under some restricted and theoretical guarantee conditions.
Zha, Zhiyuan +6 more
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Application of Weighted Truncated p Norm in Motion Target Detection [PDF]
Jisuanji gongcheng, 2018 In the moving object detection methods base on low rank and sparse decomposition,the nuclear norm is not the best approximation of the rank function of the matrix,meanwhile,the spatial continuity of moving object is not to be considered.As a result,the ...
XUAN Xiao,YU Qin
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Mathematics, 2022 The target detection ability of an infrared small target detection (ISTD) system is advantageous in many applications. The highly varied nature of the background image and small target characteristics make the detection process extremely difficult.
Sur Singh Rawat +4 more
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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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