Frequency Interpolation of the Electromagnetic Surface Currents Via Singular Value Decomposition. Application to High-Frequency Analysis

2008 ◽  
Vol 56 (12) ◽  
pp. 3789-3796 ◽  
Author(s):  
Bruno Stupfel
Keyword(s):  
Singular Value Decomposition ◽  
Frequency Analysis ◽  
High Frequency ◽  
Singular Value ◽  
Surface Currents ◽  
Value Decomposition

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.

Tube Wave Suppression in High Frequency Mine Seismic Data by Singular Value Decomposition

1995 ◽  
Vol 26 (2-3) ◽  
pp. 512-517 ◽  
Author(s):  
Geraldine Teakle ◽  
Shunhua Coa ◽  
Stewart Greenhalgh
Keyword(s):  
Singular Value Decomposition ◽  
Seismic Data ◽  
High Frequency ◽  
Singular Value ◽  
Tube Wave ◽  
Value Decomposition

scholarly journals A Robust Medical Images Watermarking Using FDCuT-DCT-SVD

2020 ◽  
Vol 13 (6) ◽  
pp. 266-278
Author(s):  
Ledya Novamizanti ◽  
◽  
Ida Wahidah ◽  
Ni Wardana ◽  
◽  
...  
Keyword(s):  
Singular Value Decomposition ◽  
Discrete Cosine Transform ◽  
High Frequency ◽  
Medical Image ◽  
Medical Images ◽  
Singular Values ◽  
Singular Value ◽  
Watermark Image ◽  
Value Decomposition

One way to prevent image duplication is by applying watermarking techniques. In this work, the watermarking process is applied to medical images using the Fast Discrete Curvelet Transforms (FDCuT), Discrete Cosine Transform (DCT), and Singular Value Decomposition (SVD) methods. The medical image of the host is transformed using FDCuT so that three subbands are obtained. High Frequency (HF) subband selected for DCT and SVD applications. Meanwhile, SVD was also applied to the watermark image. The singular value on the host image is exchanged with the singular value on the watermark. Insertion of tears by exchanging singular values does not cause the quality of medical images to decrease significantly. The experimental results prove that the proposed FDCuT-DCT-SVD algorithm produces good imperceptibility. The proposed algorithm is also resistant to various types of attacks, including JPEG compression, noise enhancement attacks, filtering attacks, and other common attacks.

scholarly journals Multi-Focus Color Image Fusion Based on Quaternion Multi-Scale Singular Value Decomposition

2021 ◽  
Vol 15 ◽  
Author(s):  
Hui Wan ◽  
Xianlun Tang ◽  
Zhiqin Zhu ◽  
Bin Xiao ◽  
Weisheng Li
Keyword(s):  
Singular Value Decomposition ◽  
Image Fusion ◽  
High Frequency ◽  
Color Image ◽  
Low Frequency ◽  
Singular Value ◽  
Activity Level ◽  
Local Contrast ◽  
Multi Scale ◽  
Value Decomposition

Most existing multi-focus color image fusion methods based on multi-scale decomposition consider three color components separately during fusion, which leads to inherent color structures change, and causes tonal distortion and blur in the fusion results. In order to address these problems, a novel fusion algorithm based on the quaternion multi-scale singular value decomposition (QMSVD) is proposed in this paper. First, the multi-focus color images, which represented by quaternion, to be fused is decomposed by multichannel QMSVD, and the low-frequency sub-image represented by one channel and high-frequency sub-image represented by multiple channels are obtained. Second, the activity level and matching level are exploited in the focus decision mapping of the low-frequency sub-image fusion, with the former calculated by using local window energy and the latter measured by the color difference between color pixels expressed by a quaternion. Third, the fusion results of low-frequency coefficients are incorporated into the fusion of high-frequency sub-images, and a local contrast fusion rule based on the integration of high-frequency and low-frequency regions is proposed. Finally, the fused images are reconstructed employing inverse transform of the QMSVD. Simulation results show that image fusion using this method achieves great overall visual effects, with high resolution images, rich colors, and low information loss.

scholarly journals Improved EEMD Denoising Method Based on Singular Value Decomposition for the Chaotic Signal

2016 ◽  
Vol 2016 ◽  
pp. 1-14 ◽  
Author(s):  
Xiulei Wei ◽  
Ruilin Lin ◽  
Shuyong Liu ◽  
Chunhui Zhang
Keyword(s):  
Singular Value Decomposition ◽  
High Frequency ◽  
Low Frequency ◽  
Chaotic Signal ◽  
Singular Value ◽  
Filtering Method ◽  
Chaotic Vibration ◽  
Noise Energy ◽  
Frequency Components ◽  
Value Decomposition

Chaotic data analysis is important in many areas of science and engineering. However, the chaotic signals are inevitably contaminated by complicated noise in the collection process which greatly interferes with the analysis of chaos identification. The chaotic vibration is extremely nonlinear and has a broad range of frequencies; linear filtering methods are not effective for chaotic signal noise reduction. Then an improved ensemble empirical mode decomposition (EEMD) based on singular value decomposition (SVD) and Savitzky-Golay (SG) filtering method was proposed. Firstly, the noise energy of first level intrinsic mode function (IMF) was estimated by “3σ” criterion, and then SVD was used to extract the signal details from first IMF, and the singular value was selected to reconstruct the IMF according to noise energy of the first IMF. Secondly, the remaining IMFs are divided into high frequency and low frequency components based on consecutive mean square error (CMSE), and the useful signals of high frequency components and low frequency components are extracted based on SVD and SG filtering method, respectively. The superiority of the proposed method is demonstrated with simulated signal, two-degree-of-freedom chaotic vibration signals, and the experimental signals based on double potential well theory.

Envelope Signal of Partial Discharge Pattern Recognition Based on Wavelet Packet Transform

2013 ◽  
Vol 823 ◽  
pp. 536-540
Author(s):  
Dong Song Luo ◽  
Kun Peng Chen
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Singular Value Decomposition ◽  
Bp Neural Network ◽  
High Frequency ◽  
Wavelet Packet ◽  
Wavelet Packet Transform ◽  
Singular Value ◽  
Envelope Signal ◽  
Value Decomposition

In order to achieve the GIS fault detection and defect type recognition, four typical defect models were designed and discharge tests are carried out aiming at insulation defect as well as discharge characteristics in the GIS .With a large number of ultra high frequency envelope signal ,a method of domain feature extraction was proposed based on wavelet packet transform with singular value decomposition .The envelope signal was decomposed through wavelet packet transform first in the method, then the coefficient matrix of wavelet packet transform was built in the scale ,after that feature vectors of matrix were extracted by means of singular value decomposition. On this basis, BP neural network was took advantage of for pattern recognition .The results show that the good recognition effect was obtained with that method . Keyword: Ultra high frequency; Envelope signal; Wavelet packet transform; Singular value decomposition; BP neural network

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