Using Singular Value Decomposition to analyse a low frequency contribution on human cortical bone with a 1MHz axial transmission probe

2008 ◽  
Vol 123 (5) ◽  
pp. 3635-3635
Author(s):  
Magali Sasso ◽  
Maryline Talmant ◽  
Guillaume Haiat ◽  
Pascal Laugier ◽  
Salah Naili
Keyword(s):  
Singular Value Decomposition ◽  
Cortical Bone ◽  
Low Frequency ◽  
Singular Value ◽  
Human Cortical Bone ◽  
Value Decomposition ◽  
Axial Transmission

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.

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.

Research and Simulation of Performance of W-SVD Watermarking Algorithm Based on Color Images

2014 ◽  
Vol 513-517 ◽  
pp. 1980-1983
Author(s):  
Hou Xiao Fang ◽  
Zhang Chun E
Keyword(s):  
Singular Value Decomposition ◽  
Digital Watermarking ◽  
Wavelet Decomposition ◽  
Low Frequency ◽  
Singular Value ◽  
Perceptual Quality ◽  
Discrete Wavelet ◽  
Discrete Wavelet Decomposition ◽  
Value Decomposition ◽  
Research And Design

The paper presents an algorithm for digital watermarking in colored image, which combined with singular value decomposition and discrete wavelet transform, it selects R-vector of colored image to do discrete wavelet decomposition, and extracts the low-frequency coefficients to finish singular value decomposition and generate watermark template, then reconstructs image after embed watermarking, completes the process of embedding watermarking. The algorithm makes full use of the excellent properties of the wavelet and singular value decomposition, which makes good performance for watermarking system. To this algorithm, the paper studied from two aspects of the image perceptual quality and robustness, and analyze the influence of the parameters in the performance of watermarking algorithm, a number of experiments and data show that these values may bring essential influence to the performance of digital watermarking system, also explains the importance of value selection in the algorithm. The conclusion of the paper is typical and universal, has certain reference significance to research and design the watermarking algorithm.

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