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.

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.

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

Brain Image Enhancement Approach Based on Singular Value Decomposition in Nonsubsampled Shearlet Transform Domain

2020 ◽  
Vol 10 (8) ◽  
pp. 1785-1794 ◽  
Author(s):  
Liangliang Li ◽  
Yujuan Si ◽  
Linli Wang ◽  
Zhenhong Jia ◽  
Hongbing Ma
Keyword(s):  
Image Enhancement ◽  
Low Frequency ◽  
Frequency Component ◽  
Singular Value ◽  
Transform Domain ◽  
Brain Images ◽  
Brain Image ◽  
Frequency Components ◽  
Definition Of ◽  
Value Decomposition

In this work, a novel image enhancement algorithm using NSST and SVD is proposed to improve the definition of the acquired brain images. The input brain image is computed by CLAHE, then the processed brain image and input brain image are decomposed into low- and high-frequency components by NSST, the singular value matrix of the low-frequency component is estimated. The final enhancement image is obtained by inverse NSST. Results of this experiment demonstrate that the proposed technique has good performance in terms of brain image enhancement when compared to other methods.

Tamper Detection and Self-Recovery Watermarking Scheme Based on DWT-SVD

2011 ◽  
Vol 225-226 ◽  
pp. 614-618
Author(s):  
Yu Ping Hu ◽  
Jun Zhang ◽  
Hua Yin ◽  
Yi Chun Liu ◽  
Ying Hong Liang
Keyword(s):  
High Frequency ◽  
Wavelet Transformation ◽  
Low Frequency ◽  
Singular Value ◽  
Tamper Detection ◽  
Intentional Content ◽  
Discrete Wavelet ◽  
Watermark Embedding ◽  
Value Decomposition ◽  
Watermarking Scheme

This paper studied the image tamper detection and recovery watermarking scheme based on the discrete wavelet transformation(DWT) and the singular value decomposition (SVD).By the property of DWT and SVD , we design two watermarks which are embedded into the high-frequency bands of the DWT domain.One watermark is from the U component of the SVD domain and used for detecting the intentional content modification and indicating the modified location, and another watermark is from the low-frequency of DWT and used for recovering the image. The watermark generation and watermark embedding are disposed in the image itself. The experimental results show that the proposed scheme can resist the mild modifications of digital image and be able to detect and recovery the malicious modifications precisely.

Performance Comparison of Dense Frequency Identifications for Empirical Mode Decomposition

2011 ◽  
Vol 378-379 ◽  
pp. 266-269
Author(s):  
Min Zheng ◽  
Fan Shen
Keyword(s):  
Singular Value Decomposition ◽  
Empirical Mode Decomposition ◽  
Wavelet Packet ◽  
Performance Comparison ◽  
Wavelet Packet Transform ◽  
Singular Value ◽  
Simulated Signal ◽  
Mode Decomposition ◽  
Frequency Components ◽  
Value Decomposition

Empirical Mode Decomposition(EMD) suffers some difficulties in separating dense frequencies. The Wavelet Packet Transform (WPT) and Singular-Value Decomposition (SVD) as signal preprocessors were used to decompose a simulated signal with dense frequency components and the performances of two signal preprocess technologies were compared in this paper. The results show that Singular-Value Decomposition (SVD) as preprocessor was better in separating dense frequencies than Wavelet Packet Transform (WPT).

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