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.

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.

Feature frequency extraction based on singular value decomposition and its application on rotor faults diagnosis

2019 ◽  
Vol 25 (6) ◽  
pp. 1246-1262 ◽  
Author(s):  
Zhen Li ◽  
Weiguang Li ◽  
Xuezhi Zhao
Keyword(s):  
Singular Value Decomposition ◽  
Singular Values ◽  
Frequency Component ◽  
Singular Value ◽  
Frequency Separation ◽  
Rotor Faults ◽  
Frequency Components ◽  
Value Decomposition ◽  
Svd Method ◽  
Feature Frequency

The selection of effective singular values using the singular value decomposition (SVD) method has always been a hot topic. In this paper, we found that there was a special relationship between effective singular values and feature frequency components. Theoretical derivations illustrated that each frequency component produced two adjacent nonzero singular values with one ranking another closely. Size of singular values was directly proportional to amplitude of feature frequency. The number of singular values was only related to the number of feature frequency components. For these discoveries, a novel feature frequency separation method based on SVD was proposed, through which axis orbits of large rotating machines were readily purified. The results show that the algorithm was very accurate in feature frequency extraction.

Underwater Image Enhancement Method Combining color and Luminance

2020 ◽  
Vol 13 ◽  
Author(s):  
ZHAO Baiting ◽  
WANG Feng ◽  
JIA Xiaofen ◽  
GUO Yongcun ◽  
WANG Chengjun
Keyword(s):  
Image Enhancement ◽  
Color Space ◽  
Low Frequency ◽  
Brightness Enhancement ◽  
Color Distortion ◽  
Underwater Image ◽  
Enhancement Method ◽  
Frequency Components ◽  
Image Contour ◽  
Objective Indicators

Background:: Aiming at the problems of color distortion, low clarity and poor visibility of underwater image caused by complex underwater environment, a wavelet fusion method UIPWF for underwater image enhancement is proposed. Methods:: First of all, an improved NCB color balance method is designed to identify and cut the abnormal pixels, and balance the color of R, G and B channels by affine transformation. Then, the color correction map is converted to CIELab color space, and the L component is equalized with contrast limited adaptive histogram to obtain the brightness enhancement map. Finally, different fusion rules are designed for low-frequency and high-frequency components, the pixel level wavelet fusion of color balance image and brightness enhancement image is realized to improve the edge detail contrast on the basis of protecting the underwater image contour. Results:: The experiments demonstrate that compared with the existing underwater image processing methods, UIPWF is highly effective in the underwater image enhancement task, improves the objective indicators greatly, and produces visually pleasing enhancement images with clear edges and reasonable color information. Conclusion:: The UIPWF method can effectively mitigate the color distortion, improve the clarity and contrast, which is applicable for underwater image enhancement in different environments.

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 Through-Wall Image Enhancement Based on Singular Value Decomposition

2012 ◽  
Vol 2012 ◽  
pp. 1-20 ◽  
Author(s):  
Muhammad Mohsin Riaz ◽  
Abdul Ghafoor
Keyword(s):  
Singular Value Decomposition ◽  
Image Enhancement ◽  
Visual Inspection ◽  
Signal To Noise Ratio ◽  
Singular Values ◽  
Singular Value ◽  
Information Theoretic ◽  
Value Decomposition ◽  
Suppress Noise ◽  
Wall Imaging

Singular value decomposition and information theoretic criterion-based image enhancement is proposed for through-wall imaging. The scheme is capable of discriminating target, clutter, and noise subspaces. Information theoretic criterion is used with conventional singular value decomposition to find number of target singular values. Furthermore, wavelet transform-based denoising is performed (to further suppress noise signals) by estimating noise variance. Proposed scheme works also for extracting multiple targets in heavy cluttered through-wall images. Simulation results are compared on the basis of mean square error, peak signal to noise ratio, and visual inspection.

A Study of Application Cosine Similarity and HOSVD for Questionnaire Data

2015 ◽  
Vol 19 (1) ◽  
pp. 100-108
Author(s):  
YosukeWatanabe ◽  
◽  
Tomohiro Yoshikawa ◽  
Takeshi Furuhashi
Keyword(s):  
Singular Value ◽  
Marketing Strategies ◽  
Cosine Similarity ◽  
The Other ◽  
Questionnaire Data ◽  
Proper Definition ◽  
Definition Of ◽  
Value Decomposition ◽  
Web Questionnaire ◽  
Similarity Matrices

Companies often carry out questionnaires, in order to gain a better grasp of consumer trends for the design of marketing strategies. The proper definition of a set of questions presents some difficulties. For example, if respondents take the meaning of two or more questions in one questionnaire to have the same/similar meanings, these questions could be rendered redundant. However, it is difficult to know beforehand how a respondent will interpret the meaning of a question. On the other hand, it is possible to assess the meaning that respondents assumed for questions, and how appropriate the set of questions is, in retrospect. In this paper, we propose a method for visualizing groups of respondents who assumed distinct meanings for questions, by applying Higher Order Singular Value Decomposition (HOSVD) to a tensor consisting of cosine similarity matrices. The proposed method is applied to a Web questionnaire dataset, and it is shown that the new method can identify the respondents’ unique understanding of the meanings of questions, which are not found using the conventional method. We also show that the proposedmethod is the most effective for the visualization of relationships between questions, among the possible ways of applying HOSVD to cosine similarity matrices.

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