Results 41 to 50 of about 1,958,095 (294)
NOWNUNM: Nonlocal Weighted Nuclear Norm Minimization for Sparse-Sampling CT Reconstruction
IEEE Access, 2018 Computed tomography (CT) image reconstruction using classical total variation (TV)-based methods or its variations inevitably suffers from a blocky effect when the sampling number is low, because of the piecewise assumption. A low-rank based method is an
Yi Zhang +5 more
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A nullspace analysis of the nuclear norm heuristic for rank minimization [PDF]
2010 IEEE International Conference on Acoustics, Speech and Signal Processing, 2010 The problem of minimizing the rank of a matrix subject to linear equality constraints arises in applications in machine learning, dimensionality reduction, and control theory, and is known to be NP-hard. A popular heuristic minimizes the nuclear norm (sum of the singular values) of the matrix instead of the rank, and was recently shown to give an exact
Krishnamurthy Dvijotham, Maryam Fazel
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Selecting Regularization Parameters for Nuclear Norm--Type Minimization Problems
SIAM Journal on Scientific Computing, 2022 The reconstruction of a low-rank matrix from its noisy observation finds its usage in many applications. It can be reformulated into a constrained nuclear norm minimization problem, where the bound $\eta$ of the constraint is explicitly given or can be estimated by the probability distribution of the noise. When the Lagrangian method is applied to find
Kexin Li +3 more
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Proximal iteratively reweighted algorithm for low-rank matrix recovery
Journal of Inequalities and Applications, 2018 This paper proposes a proximal iteratively reweighted algorithm to recover a low-rank matrix based on the weighted fixed point method. The weighted singular value thresholding problem gains a closed form solution because of the special properties of ...
Chao-Qun Ma, Yi-Shuai Ren
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IEEE Access, 2021 The resolution related with the image quality of acoustic imaging using a microphone array is limited by the size and density of the array. However, non-synchronous measurements can exceed the constraints defined by measurements with a single fixed array.
Liang Yu +5 more
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An Improved Weighted Nuclear Norm Minimization Method for Image Denoising
IEEE Access, 2019 Patch-based low rank matrix approximation has shown great potential in image denoising. Among state-of-the-art methods in this topic, the weighted nuclear norm minimization (WNNM) has been attracting significant attention due to its competitive denoising
Hyoseon Yang +3 more
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Deep Unfolding of Iteratively Reweighted ADMM for Wireless RF Sensing
Sensors, 2022 We address the detection of material defects, which are inside a layered material structure using compressive sensing-based multiple-input and multiple-output (MIMO) wireless radar.
Udaya S. K. P. Miriya Thanthrige +2 more
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Multi-channel nuclear norm minus Frobenius norm minimization for color image denoising
Signal Processing, 2023 Color image denoising is frequently encountered in various image processing and computer vision tasks. One traditional strategy is to convert the RGB image to a less correlated color space and denoise each channel of the new space separately. However, such a strategy can not fully exploit the correlated information between channels and is inadequate to
Yiwen Shan +3 more
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Massive Data Classification via Unconstrained Support Vector Machines [PDF]
, 2006 A highly accurate algorithm, based on support vector machines formulated as linear programs [13, 1], is proposed here as a completely unconstrained minimization problem [15].
O. L. Mangasarian +3 more
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Fast randomized Singular Value Thresholding for Nuclear Norm Minimization [PDF]
2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015 Rank minimization problem can be boiled down to either Nuclear Norm Minimization (NNM) or Weighted NNM (WNNM) problem. The problems related to NNM (or WNNM) can be solved iteratively by applying a closed-form proximal operator, called Singular Value Thresholding (SVT) (or Weighted SVT), but they suffer from high computational cost to compute a Singular
Tae Hyun Oh +3 more
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