An Effective Filtering Technique for Image Denoising Using Probabilistic Principal Component Analysis (PPCA)

Mredhula, L.; Dorairangaswamy, M. A.

doi:10.1166/jmihi.2016.1602

Cited by 8 publications

(3 citation statements)

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“…Although pPCA has previously been employed in various areas of research, e.g. estimation of the EOF's for satellite-derived sea surface temperature (SST) data (Houseago-Stokes and Challenor 2004), a study on the precipitation and absorption squeeze (Andrei and Malandrino 2003), generation of the video textures (Fan and Bouguila 2009), detection of a small target (Cao et al 2008), investigation of traffic flow volume (Qu et al 2009), managing self-organizing maps (Lopez-Rubio et al 2009), detection of outliers (Chen et al 2009), tracking of the objects (Xiang et al 2012), speaker recognition (Madikeri 2014), investigation of the nonlinear distributed parameter processes (Qi et al 2012), nonlinear sensor fault diagnosis (Sharifi and Langari 2017), studying trends of mean temperatures and warm extremes (Moron et al 2016), denoising of images (Mredhula and Dorairangaswamy 2016) or detection of the rolling element bearing fault (Xiang et al 2015), according to the best of our knowledge, the pPCA filtering that is readily adapted to the position time series with missing data, has been presented for GNSS residuals (either position or ZTD) for the first time.…”

Section: Cmementioning

confidence: 99%

A Filtering of Incomplete GNSS Position Time Series with Probabilistic Principal Component Analysis

Gruszczynski¹,

Kłos²,

Bogusz³

2018

Pageoph Topical Volumes

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For the first time, we introduced the probabilistic principal component analysis (pPCA) regarding the spatio-temporal filtering of Global Navigation Satellite System (GNSS) position time series to estimate and remove Common Mode Error (CME) without the interpolation of missing values. We used data from the International GNSS Service (IGS) stations which contributed to the latest International Terrestrial Reference Frame (ITRF2014). The efficiency of the proposed algorithm was tested on the simulated incomplete time series, then CME was estimated for a set of 25 stations located in Central Europe. The newly applied pPCA was compared with previously used algorithms, which showed that this method is capable of resolving the problem of proper spatio-temporal filtering of GNSS time series characterized by different observation time span. We showed, that filtering can be carried out with pPCA method when there exist two time series in the dataset having less than 100 common epoch of observations. The 1st Principal Component (PC) explained more than 36% of the total variance represented by time series residuals' (series with deterministic model removed), what compared to the other PCs variances (less than 8%) means that common signals are significant in GNSS residuals. A clear improvement in the spectral indices of the power-law noise was noticed for the Up component, which is reflected by an average shift towards white noise from-0.98 to-0.67 (30%). We observed a significant average reduction in the accuracy of stations' velocity estimated for filtered residuals by 35, 28 and 69% for the North, East, and Up components, respectively. CME series were also subjected to analysis in the context of environmental mass loading influences of the filtering results. Subtraction of the environmental loading models from GNSS residuals provides to reduction of the estimated CME variance by 20 and 65% for horizontal and vertical components, respectively.

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Section: Cmementioning

confidence: 99%

A Filtering of Incomplete GNSS Position Time Series with Probabilistic Principal Component Analysis

Gruszczynski¹,

Kłos²,

Bogusz³

2018

Pageoph Topical Volumes

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“…Therefore, we can use the shape features to suppress clutters and highlight the fissure representation. To achieve this purpose, a morphology and connected component-based region operator like the Matlab function 'regionprops' was used to calculate the selected object property [17,18] such as the major axis length H and minor axis length W. We used S to represent each object in the axial, coronal, sagittal or diagonal planes. To suppress the ellipse-like structures, a line measure criterion was defined:…”

Section: Multiple Section Modelmentioning

confidence: 99%

Pulmonary Fissure Detection in 3D CT Images Using a Multiple Section Model

Xiao

Zhou

2019

Algorithms

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As a typical landmark in human lungs, the detection of pulmonary fissures is of significance to computer aided diagnosis and surgery. However, the automatic detection of pulmonary fissures in CT images is a difficult task due to complex factors like their 3D membrane shape, intensity variation and adjacent interferences. Based on the observation that the fissure object often appears as thin curvilinear structures across 2D section images, we present an efficient scheme to solve this problem by merging the fissure line detection from multiple cross-sections in different directions. First, an existing oriented derivative of stick (ODoS) filter was modified for pulmonary fissure line enhancement. Then, an orientation partition scheme was applied to suppress the adhering clutters. Finally, a multiple section model was proposed for pulmonary fissure integration and segmentation. The proposed method is expected to improve fissure detection by extracting more weak objects while suppressing unrelated interferences. The performance of our scheme was validated in experiments using the publicly available open Lobe and Lung Analysis 2011 (LOLA11) dataset. Compared with manual references, the proposed scheme achieved a high segmentation accuracy, with a median F1-score of 0.8916, which was much better than conventional methods.

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“…Because the Higuchi approach is sensitive to noise (Esteller et al, 2001), it is a better strategy to use it in combination with a denoising technique. Probabilistic principal component analysis (PPCA) is a conventional denoising model (Chen et al, 2016; Mredhula and Dorairangaswamy, 2016), and the denoising process is realized by governing the residual variance of discard dimension when reconstructing the observation data from the principal subspace. Hu et al (2018) proposed a probabilistic principal component analysis–empirical wavelet transform method to successfully extract the bearing fault.…”

Section: Introductionmentioning

confidence: 99%

On multi-fault detection of rolling bearing through probabilistic principal component analysis denoising and Higuchi fractal dimension transformation

Yang

Xiang

Jiang

2021

Journal of Vibration and Control

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Bearing multi-fault detection from stochastic vibration signal is still a thorny task to dispose of because of the complex interplay between different fault components under severe noise interference. In such case, conventional techniques such as filter processing and envelope demodulation may cause undesired results. To overcome the limitation, this article explores a filtering-free technique combined probabilistic principal component analysis denoising with the Higuchi fractal dimension transformation to diagnose the bearing multi-faults. Fractal theory is used to optimize the model parameters and stabilize the random vibrational signal for fast Fourier transform spectrum analysis. Noise interference in the Higuchi transformation is capped using a probabilistic principal component analysis model whose parameters are optimized through embedding dimension Cao algorithm and correlation dimension Grassberger and Procaccia algorithm. The fault diagnostic scheme mainly falls into three steps. First, the original vibration signal is truncated into a series of sub-signal segments by moving window whose length is determined as twice the value of maximum time delay that is provided by examining the steady Higuchi fractal dimension value of a raw signal in a process of plotting the fractal dimension over a range of time delay. Then, the Higuchi approach is used to estimate the average fractal dimension for each segment to create a quasi-stationary Higuchi fractal dimension sequence on which, finally, the fault features are straightforwardly extracted by the fast Fourier transform algorithm. The effectiveness of the proposed method is validated using simulated and experimental compound bearing fault vibration signals. Some fault components may be clouded if applied Higuchi fractal dimension alone because of the noise interference, but using the probabilistic principal component analysis–Higuchi fractal dimension method leads to clear diagnostic results. It indicates that the proposed approach can be incorporated into bearing multi-fault extraction from raw vibration signals.

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An Effective Filtering Technique for Image Denoising Using Probabilistic Principal Component Analysis (PPCA)

Cited by 8 publications

References 0 publications

A Filtering of Incomplete GNSS Position Time Series with Probabilistic Principal Component Analysis

A Filtering of Incomplete GNSS Position Time Series with Probabilistic Principal Component Analysis

Pulmonary Fissure Detection in 3D CT Images Using a Multiple Section Model

On multi-fault detection of rolling bearing through probabilistic principal component analysis denoising and Higuchi fractal dimension transformation

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