A Digital Impulse Noise Removal Filter

Hillig, Kathy J. Dien; Morris, Michael D.

doi:10.1366/0003702824639231

Cited by 16 publications

(10 citation statements)

References 2 publications

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“…Nonlinear smoothing algorithms have been used to resolve the problem of spikes in Raman spectra. Hillig and Morris [11] work out the procedure which approximates each points in the spectrum by means of a cubic spline in a local region around, but not including that point. If the derivation between the observed and predicted values is greater in magnitude than a predetermined limit, the observed point is replaced by a point closer to the spline approximation.…”

Section: The Procedures For Spikes Removalmentioning

confidence: 99%

Removing Spikes from Voltammetric Curves in the Presence of Random Noise

Jakubowska

Kubiak

2005

Electroanalysis

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The presence of noises and spikes in the experimental curve generates problems in the process of visualization and interpretation of the data. The procedure for spikes removal from voltammetric curves which are additionally distorted by random noise of different amplitude is proposed. The developed method bases on the assumption that spikes are measuring points corrupted by the gross errors. The statistical tests for detecting such errors are applied. Recognized spikes are replaced by points obtained by polynomial interpolation. The method eliminates close to 100% of spikes and reconstructs the ideal curve shape better than other algorithms. The considered new procedure gives the improvement of analytical parameters and may be applied for the set of curves in voltammetric experiment. Because the method is statistically valid it may be used for automatic spikes removal in intelligent computerized measuring systems in analytical chemistry.

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Section: The Procedures For Spikes Removalmentioning

confidence: 99%

Removing Spikes from Voltammetric Curves in the Presence of Random Noise

Jakubowska

Kubiak

2005

Electroanalysis

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“…Several smoothing techniques have been proposed in this sense. 12,–16 In some of those approaches, the corrected spectrum after the denoising treatment might show a slight undesirable distortion in the rest of the data.…”

Section: Introductionmentioning

confidence: 99%

Autocorrelation Analysis Combined with a Wavelet Transform Method to Detect and Remove Cosmic Rays in a Single Raman Spectrum

Maury

Revilla

2015

Appl Spectrosc

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Cosmic rays (CRs) occasionally affect charge-coupled device (CCD) detectors, introducing large spikes with very narrow bandwidth in the spectrum. These CR features can distort the chemical information expressed by the spectra. Consequently, we propose here an algorithm to identify and remove significant spikes in a single Raman spectrum. An autocorrelation analysis is first carried out to accentuate the CRs feature as outliers. Subsequently, with an adequate selection of the threshold, a discrete wavelet transform filter is used to identify CR spikes. Identified data points are then replaced by interpolated values using the weighted-average interpolation technique. This approach only modifies the data in a close vicinity of the CRs. Additionally, robust wavelet transform parameters are proposed (a desirable property for automation) after optimizing them with the application of the method in a great number of spectra. However, this algorithm, as well as all the single-spectrum analysis procedures, is limited to the cases in which CRs have much narrower bandwidth than the Raman bands. This might not be the case when low-resolution Raman instruments are used.

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“…Because of the large number of acquisitions required for some Raman mapping experiments, a fast post-collection method is required that can detect and remove enough CRs so that PCA is not influenced by the remaining ones. Most post-collection cosmic ray filters are based on smoothing algorithms in which outlying intensities are identified by their deviation from intensities measured at neighboring wavenumbers, [3][4][5][6] or they are removed by applying median filters. 7,8 Therefore, they are based on the assumption that CRs are significantly narrower or far more intense than Raman bands.…”

Section: Introductionmentioning

confidence: 99%

Removing Cosmic Ray Features from Raman Map Data by a Refined Nearest Neighbor Comparison Method as a Precursor for Chemometric Analysis

2010

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An algorithm to remove cosmic ray (CR) features from Raman spectra collected in mapping experiments using a charge-coupled device (CCD) is presented. Each spectrum is compared to spectra collected from adjacent points in space using correlation values. The most similar neighbor (MSN) spectrum is selected, offset, and used for identification of CRs. The offset values are defined in terms of the noise level for data with a low signal-to-noise ratio and in terms of the peak height for data with a high signal-to-noise ratio. Scaled intensity values of the MSN spectra are used for replacement of contaminated pixels, allowing for full recovery of underlying spectral features. The algorithm is applicable for any Raman map where the particle sizes within the analyzed mixture are larger than the sampling size or to any other data where the sampling is more frequent than the variation, e.g., time series or temperature profiles. Its application to several maps of pharmaceutical samples is discussed here. With an appropriate offset value for the MSN spectra, no misdetections occur, and all CRs more intense than the offset are removed, which includes the CRs that would have hampered subsequent chemometric analysis by methods such as principal component analysis (PCA).

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A Digital Impulse Noise Removal Filter

Cited by 16 publications

References 2 publications

Removing Spikes from Voltammetric Curves in the Presence of Random Noise

Removing Spikes from Voltammetric Curves in the Presence of Random Noise

Autocorrelation Analysis Combined with a Wavelet Transform Method to Detect and Remove Cosmic Rays in a Single Raman Spectrum

Removing Cosmic Ray Features from Raman Map Data by a Refined Nearest Neighbor Comparison Method as a Precursor for Chemometric Analysis

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