scholarly journals Image Denoising Using Hybrid Singular Value Thresholding Operators

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 8157-8165 ◽  
Author(s):  
Fan Zhang ◽  
Hui Fan ◽  
Peiqiang Liu ◽  
Jinjiang Li
Keyword(s):  
Image Denoising ◽  
Singular Value ◽  
Singular Value Thresholding

scholarly journals Image Denoising Based on Nonlocal Bayesian Singular Value Thresholding and Stein’s Unbiased Risk Estimator

2019 ◽  
Vol 28 (10) ◽  
pp. 4899-4911 ◽  
Author(s):  
Caoyuan Li ◽  
Hong-Bo Xie ◽  
Xuhui Fan ◽  
Richard Yi Da Xu ◽  
Sabine Van Huffel ◽  
...  
Keyword(s):  
Image Denoising ◽  
Singular Value ◽  
Risk Estimator ◽  
Unbiased Risk ◽  
Singular Value Thresholding

scholarly journals A Singular Value Thresholding Algorithm for Matrix Completion

2010 ◽  
Vol 20 (4) ◽  
pp. 1956-1982 ◽  
Author(s):  
Jian-Feng Cai ◽  
Emmanuel J. Candès ◽  
Zuowei Shen
Keyword(s):  
Matrix Completion ◽  
Singular Value ◽  
Thresholding Algorithm ◽  
Singular Value Thresholding

scholarly journals An Image Denoising Method with Enhancement of the Directional Features Based on Wavelet and SVD Transforms

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Min Wang ◽  
Zhen Li ◽  
Xiangjun Duan ◽  
Wei Li
Keyword(s):  
Wavelet Transform ◽  
Singular Value Decomposition ◽  
Image Denoising ◽  
High Frequency ◽  
Low Frequency ◽  
Singular Value ◽  
Denoising Method ◽  
Diagonal Part ◽  
Value Decomposition ◽  
Image Part

This paper proposes an image denoising method, using the wavelet transform and the singular value decomposition (SVD), with the enhancement of the directional features. First, use the single-level discrete 2D wavelet transform to decompose the noised image into the low-frequency image part and the high-frequency parts (the horizontal, vertical, and diagonal parts), with the edge extracted and retained to avoid edge loss. Then, use the SVD to filter the noise of the high-frequency parts with image rotations and the enhancement of the directional features: to filter the diagonal part, one needs first to rotate it 45 degrees and rotate it back after filtering. Finally, reconstruct the image from the low-frequency part and the filtered high-frequency parts by the inverse wavelet transform to get the final denoising image. Experiments show the effectiveness of this method, compared with relevant methods.

Anode effect prediction based on a singular value thresholding and extreme gradient boosting approach

2018 ◽  
Vol 30 (1) ◽  
pp. 015104 ◽  
Author(s):  
Kai-Bo Zhou ◽  
Zhi-Xin Zhang ◽  
Jie Liu ◽  
Zhong-Xu Hu ◽  
Xiao-Kun Duan ◽  
...  
Keyword(s):  
Singular Value ◽  
Anode Effect ◽  
Gradient Boosting ◽  
Extreme Gradient Boosting ◽  
Singular Value Thresholding

scholarly journals A Singular Value Thresholding with Diagonal-Update Algorithm for Low-Rank Matrix Completion

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Yong-Hong Duan ◽  
Rui-Ping Wen ◽  
Yun Xiao
Keyword(s):  
Matrix Completion ◽  
Arithmetic Operation ◽  
Computational Cost ◽  
Singular Value ◽  
Low Rank ◽  
Simple Arithmetic ◽  
Rank Matrix ◽  
Low Rank Matrix Completion ◽  
Low Rank Matrix ◽  
Singular Value Thresholding

The singular value thresholding (SVT) algorithm plays an important role in the well-known matrix reconstruction problem, and it has many applications in computer vision and recommendation systems. In this paper, an SVT with diagonal-update (D-SVT) algorithm was put forward, which allows the algorithm to make use of simple arithmetic operation and keep the computational cost of each iteration low. The low-rank matrix would be reconstructed well. The convergence of the new algorithm was discussed in detail. Finally, the numerical experiments show the effectiveness of the new algorithm for low-rank matrix completion.

Fuzzy singular value shrinkage for Poisson image denoising

2019 ◽  
Vol 66 (15) ◽  
pp. 1569-1578
Author(s):  
Huaiguang Chen ◽  
Shujun Fu ◽  
Hong Wang ◽  
Lin Zhai ◽  
Fengling Wang ◽  
...  
Keyword(s):  
Image Denoising ◽  
Singular Value

scholarly journals Efficient quantum circuit for singular-value thresholding

2018 ◽  
Vol 98 (1) ◽  
Author(s):  
Bojia Duan ◽  
Jiabin Yuan ◽  
Ying Liu ◽  
Dan Li
Keyword(s):  
Quantum Circuit ◽  
Singular Value ◽  
Singular Value Thresholding
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