scholarly journals Fusion of External and Internal Prior Information for the Removal of Gaussian Noise in Images

2020 ◽  
Vol 6 (10) ◽  
pp. 103
Author(s):  
Ali S. Awad
Keyword(s):  
Gaussian Noise ◽  
Prior Information ◽  
Signal To Noise Ratio ◽  
Principal Component ◽  
The Other ◽  
Noisy Image ◽  
Simulation Experiments ◽  
Two Dimensional Image ◽  
Pixel Location ◽  
Image Pixels

In this paper, a new method for the removal of Gaussian noise based on two types of prior information is described. The first type of prior information is internal, based on the similarities between the pixels in the noisy image, and the other is external, based on the index or pixel location in the image. The proposed method focuses on leveraging these two types of prior information to obtain tangible results. To this end, very similar patches are collected from the noisy image. This is done by sorting the image pixels in ascending order and then placing them in consecutive rows in a new two-dimensional image. Henceforth, a principal component analysis is applied on the patch matrix to help remove the small noisy components. Since the restored pixels are similar or close in values to those in the clean image, it is preferable to arrange them using indices similar to those of the clean pixels. Simulation experiments show that outstanding results are achieved, compared to other known methods, either in terms of image visual quality or peak signal to noise ratio. Specifically, once the proper indices are used, the proposed method achieves PSNR value better than the other well-known methods by >1.5 dB in all the simulation experiments.

scholarly journals A Novel Radar HRRP Recognition Method with Accelerated T-Distributed Stochastic Neighbor Embedding and Density-Based Clustering

Sensors ◽  
2019 ◽  
Vol 19 (23) ◽  
pp. 5112 ◽  
Author(s):  
Hao Wu ◽  
Dahai Dai ◽  
Xuesong Wang
Keyword(s):  
Prior Information ◽  
Signal To Noise Ratio ◽  
Recognition Performance ◽  
Principal Component ◽  
High Dimensional ◽  
Recognition Method ◽  
Range Profile ◽  
Density Based Clustering ◽  
Computation Speed ◽  
High Resolution Range Profile

High-resolution range profile (HRRP) has attracted intensive attention from radar community because it is easy to acquire and analyze. However, most of the conventional algorithms require the prior information of targets, and they cannot process a large number of samples in real time. In this paper, a novel HRRP recognition method is proposed to classify unlabeled samples automatically where the number of categories is unknown. Firstly, with the preprocessing of HRRPs, we adopt principal component analysis (PCA) for dimensionality reduction of data. Afterwards, t-distributed stochastic neighbor embedding (t-SNE) with Barnes–Hut approximation is conducted for the visualization of high-dimensional data. It proves to reduce the dimensionality, which has significantly improved the computation speed. Finally, it is exhibited that the recognition performance with density-based clustering is superior to conventional algorithms under the condition of large azimuth angle ranges and low signal-to-noise ratio (SNR).

An Improvement of Non-Local Means Denoising Method in the Presence of Large Noise

2012 ◽  
Vol 263-266 ◽  
pp. 223-226
Author(s):  
Musab Elkheir Salih ◽  
Xu Ming Zhang ◽  
Ming Yue Ding
Keyword(s):  
Gaussian Noise ◽  
Signal To Noise Ratio ◽  
Additive White Gaussian Noise ◽  
Principal Component ◽  
Singular Value ◽  
Kernel Principal Component Analysis ◽  
Denoising Method ◽  
Local Means ◽  
Non Local ◽  
Value Decomposition

The performance of singular value decomposition (SVD) based nonlocal mean (NLM) denoising method degrades when the noise is high. This paper describes an approach of an improvement of NLM denoising when the noise is large. Instead of SVD, we combine the kernel principal component analysis (KPCA) with NLM. It is demonstrated in terms of peak signal to noise ratio (PSNR) in decibels (dB) that the NLM denoising method is improved using various test images corrupted by large additive white Gaussian noise (AWGN)

scholarly journals Applying Ateb–Gabor Filters to Biometric Imaging Problems

Symmetry ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 717
Author(s):  
Mariia Nazarkevych ◽  
Natalia Kryvinska ◽  
Yaroslav Voznyi
Keyword(s):  
Wavelet Transformation ◽  
Symmetric Functions ◽  
Gabor Filter ◽  
Signal To Noise Ratio ◽  
The Other ◽  
Mean Square ◽  
Image Transformation ◽  
Gabor Filtering ◽  
Signal To Noise ◽  
Filtration Method

This article presents a new method of image filtering based on a new kind of image processing transformation, particularly the wavelet-Ateb–Gabor transformation, that is a wider basis for Gabor functions. Ateb functions are symmetric functions. The developed type of filtering makes it possible to perform image transformation and to obtain better biometric image recognition results than traditional filters allow. These results are possible due to the construction of various forms and sizes of the curves of the developed functions. Further, the wavelet transformation of Gabor filtering is investigated, and the time spent by the system on the operation is substantiated. The filtration is based on the images taken from NIST Special Database 302, that is publicly available. The reliability of the proposed method of wavelet-Ateb–Gabor filtering is proved by calculating and comparing the values of peak signal-to-noise ratio (PSNR) and mean square error (MSE) between two biometric images, one of which is filtered by the developed filtration method, and the other by the Gabor filter. The time characteristics of this filtering process are studied as well.

scholarly journals Geometric Approach to Analytic Marginalisation of the Likelihood Ratio for Continuous Gravitational Wave Searches

Universe ◽  
2021 ◽  
Vol 7 (6) ◽  
pp. 174
Author(s):  
Karl Wette
Keyword(s):  
Gravitational Wave ◽  
Likelihood Ratio ◽  
Signal To Noise Ratio ◽  
Geometric Approach ◽  
The Other ◽  
Efficient Computation ◽  
Likelihood Ratios ◽  
Signal To Noise ◽  
Noise Ratio ◽  
Optimal Signal

The likelihood ratio for a continuous gravitational wave signal is viewed geometrically as a function of the orientation of two vectors; one representing the optimal signal-to-noise ratio, and the other representing the maximised likelihood ratio or F-statistic. Analytic marginalisation over the angle between the vectors yields a marginalised likelihood ratio, which is a function of the F-statistic. Further analytic marginalisation over the optimal signal-to-noise ratio is explored using different choices of prior. Monte-Carlo simulations show that the marginalised likelihood ratios had identical detection power to the F-statistic. This approach demonstrates a route to viewing the F-statistic in a Bayesian context, while retaining the advantages of its efficient computation.

Preliminary Communication: 2: The Highlands Dependency Questionnaire (HDQ): An administered version for use with mothers of school children

1971 ◽  
Vol 1 (4) ◽  
pp. 343-347 ◽  
Author(s):  
Ian Berg ◽  
Ralph McGuire ◽  
Edward Whelan
Keyword(s):  
General Population ◽  
Secondary School ◽  
School Children ◽  
Reliability And Validity ◽  
Principal Component ◽  
The Other ◽  
Factor Analyses ◽  
Child Relationship ◽  
Preliminary Communication ◽  
Mother Child Relationship

SYNOPSISA questionnaire concerned with dependency, mainly in the mother–child relationship, and intended for use in child psychiatry, is described. It was administered to the mothers of 116 randomly selected junior and secondary school children in the general population, stratified into age, sex, social class, and school groupings. Two meaningful dimensions were revealed by principal component factor analyses: one concerned with reliance on mother and the other reflecting sociability. Reliability and validity were found to be satisfactory.

Image Denoising Using Sparse Representation and Principal Component Analysis

2021 ◽  
pp. 2250033
Author(s):  
Maryam Abedini ◽  
Horriyeh Haddad ◽  
Marzieh Faridi Masouleh ◽  
Asadollah Shahbahrami
Keyword(s):  
Principal Component Analysis ◽  
Sparse Representation ◽  
Image Denoising ◽  
Matching Pursuit ◽  
Signal To Noise Ratio ◽  
Principal Component ◽  
Structural Similarity ◽  
Component Analysis ◽  
Block Matching ◽  
Discrete Wavelet

This study proposes an image denoising algorithm based on sparse representation and Principal Component Analysis (PCA). The proposed algorithm includes the following steps. First, the noisy image is divided into overlapped [Formula: see text] blocks. Second, the discrete cosine transform is applied as a dictionary for the sparse representation of the vectors created by the overlapped blocks. To calculate the sparse vector, the orthogonal matching pursuit algorithm is used. Then, the dictionary is updated by means of the PCA algorithm to achieve the sparsest representation of vectors. Since the signal energy, unlike the noise energy, is concentrated on a small dataset by transforming into the PCA domain, the signal and noise can be well distinguished. The proposed algorithm was implemented in a MATLAB environment and its performance was evaluated on some standard grayscale images under different levels of standard deviations of white Gaussian noise by means of peak signal-to-noise ratio, structural similarity indexes, and visual effects. The experimental results demonstrate that the proposed denoising algorithm achieves significant improvement compared to dual-tree complex discrete wavelet transform and K-singular value decomposition image denoising methods. It also obtains competitive results with the block-matching and 3D filtering method, which is the current state-of-the-art for image denoising.

Peak Signal-to-Noise Ratio Evaluation of Server Display Monitors and Client Display Monitors in a Digital Subtraction Angiography Devices

2020 ◽  
Vol 3 (1) ◽  
pp. 33-41
Author(s):  
Hwunjae Lee ◽  
◽  
Junhaeng Lee ◽  
Keyword(s):  
Signal To Noise Ratio ◽  
Information Loss ◽  
The Other ◽  
Digital Subtraction ◽  
Signal Distortion ◽  
Signal To Noise ◽  
Image Storage ◽  
And Performance ◽  
Pacs Server ◽  
Display Monitor

This study evaluated PSNR of server display monitor and client display monitor of DSA system. The signal is acquired and imaged during the surgery and stored in the PACS server. After that, distortion of the original signal is an important problem in the process of observation on the client monitor. There are many problems such as noise generated during compression and image storage/transmission in PACS, information loss during image storage and transmission, and deterioration in image quality when outputting medical images from a monitor. The equipment used for the experiment in this study was P's DSA. We used two types of monitors in our experiment, one is P’s company resolution 1280×1024 pixel monitor, and the other is W’s company resolution 1536×2048 pixel monitor. The PACS Program used MARO-view, and for the experiment, a PSNR measurement program using Visual C++ was implemented and used for the experiment. As a result of the experiment, the PSNR value of the kidney angiography image was 26.958dB, the PSNR value of the lung angiography image was 28.9174 dB, the PSNR value of the heart angiography image was 22.8315dB, and the PSNR value of the neck angiography image was 37.0319 dB, and the knee blood vessels image showed a PSNR value of 43.2052 dB, respectively. In conclusion, it can be seen that there is almost no signal distortion in the process of acquiring, storing, and transmitting images in PACS. However, it suggests that the image signal may be distorted depending on the resolution and performance of each monitor. Therefore, it will be necessary to evaluate the performance of the monitor and to maintain the performance.

scholarly journals Discrimination of brazilian red varietal wines according to their sensory descriptors

2011 ◽  
Vol 35 (6) ◽  
pp. 1172-1176 ◽  
Author(s):  
Alberto Miele ◽  
Luiz Antenor Rizzon
Keyword(s):  
Principal Component Analysis ◽  
Total Variation ◽  
Principal Component ◽  
Pinot Noir ◽  
The Other ◽  
Sensory Characteristics ◽  
Cabernet Sauvignon ◽  
Sensory Panel ◽  
Red Grape ◽  
Grape Varieties

The purpose of this paper was to establish the sensory characteristics of wines made from old and newly introduced red grape varieties. To attain this objective, 16 Brazilian red varietal wines were evaluated by a sensory panel of enologists who assessed wines according to their aroma and flavor descriptors. A 90 mm unstructured scale was used to quantify the intensity of 26 descriptors, which were analyzed by means of the Principal Component Analysis (PCA). The PCA showed that three important components represented 74.11% of the total variation. PC 1 discriminated Tempranillo, Marselan and Ruby Cabernet wines, with Tempranillo being characterized by its equilibrium, quality, harmony, persistence and body, as well as by, fruity, spicy and oaky characters. The other two varietals were defined by vegetal, oaky and salty characteristics; PC 2 discriminated Pinot Noir, Sangiovese, Cabernet Sauvignon and Arinarnoa, where Pinot Noir was characterized by its floral flavor; PC 3 discriminated only Malbec, which had weak, floral and fruity characteristics. The other varietal wines did not show important discriminating effects.

On the Modeling and Simulation of Friction

1991 ◽  
Vol 113 (3) ◽  
pp. 354-362 ◽  
Author(s):  
D. A. Haessig ◽  
B. Friedland
Keyword(s):  
Modeling And Simulation ◽  
Experimental Observation ◽  
Friction Force ◽  
Closed Loop ◽  
Physical Phenomenon ◽  
Classical Model ◽  
The Other ◽  
Simulation Experiments ◽  
New Models ◽  
Integrator Model

Two new models for “slip-stick” friction are presented. One, called the “bristle model,” is an approximation designed to capture the physical phenomenon of sticking. This model is relatively inefficient numerically. The other model, called the “reset integrator model,” does not capture the details of the sticking phenomenon, but is numerically efficient and exhibits behavior similar to the model proposed by Karnopp in 1985. All three of these models and the Dahl model are preferable to the classical model, which poorly represents the friction force at zero velocity. Simulation experiments show that the Karnopp model, the Dahl model, and the new models give similar results in two examples. In a closed-loop example, the classical model predicts a limit cycle which is not observed in the laboratory. The Karnopp model, the Dahl model, and the new models, on the other hand, agree with the experimental observation.

scholarly journals Continuum Power CCA: A Unified Approach for Isolating Coupled Modes

2015 ◽  
Vol 28 (3) ◽  
pp. 1016-1030 ◽  
Author(s):  
Erik Swenson
Keyword(s):  
Signal To Noise Ratio ◽  
Full Range ◽  
Synthetic Data ◽  
Principal Component Regression ◽  
Principal Component ◽  
Accurate Estimate ◽  
Unified Approach ◽  
Coupled Modes ◽  
Multivariate Statistical ◽  
Sample Covariance

Abstract Various multivariate statistical methods exist for analyzing covariance and isolating linear relationships between datasets. The most popular linear methods are based on singular value decomposition (SVD) and include canonical correlation analysis (CCA), maximum covariance analysis (MCA), and redundancy analysis (RDA). In this study, continuum power CCA (CPCCA) is introduced as one extension of continuum power regression for isolating pairs of coupled patterns whose temporal variation maximizes the squared covariance between partially whitened variables. Similar to the whitening transformation, the partial whitening transformation acts to decorrelate individual variables but only to a partial degree with the added benefit of preconditioning sample covariance matrices prior to inversion, providing a more accurate estimate of the population covariance. CPCCA is a unified approach in the sense that the full range of solutions bridges CCA, MCA, RDA, and principal component regression (PCR). Recommended CPCCA solutions include a regularization for CCA, a variance bias correction for MCA, and a regularization for RDA. Applied to synthetic data samples, such solutions yield relatively higher skill in isolating known coupled modes embedded in noise. Provided with some crude prior expectation of the signal-to-noise ratio, the use of asymmetric CPCCA solutions may be justifiable and beneficial. An objective parameter choice is offered for regularization with CPCCA based on the covariance estimate of O. Ledoit and M. Wolf, and the results are quite robust. CPCCA is encouraged for a range of applications.

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