Wavelet filtering for analytical data

Wolkenstein, M.; Hutter, Herbert; Grasserbauer, M.

doi:10.1007/s002160050373

Cited by 10 publications

(6 citation statements)

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“…It has been shown to aid in peak definition in infrared spectra 30 and ToF-SIMS image analysis and classification. 20,31,32 Wavelet denoising is accomplished by applying a wavelet transform to the image, which decomposes the image signal into multiple levels containing varying degrees of high-to low-frequency information. Components with small wavelet coefficients contain little signal energy; omitting these components can compress and denoise the image signal while retaining the important image content.…”

Section: Introductionmentioning

confidence: 99%

Denoising and multivariate analysis of time‐of‐flight SIMS images

Wickes

Kim

Castner

2003

Surface & Interface Analysis

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Time-of-flight SIMS (ToF-SIMS) imaging offers a modality for simultaneously visualizing the spatial distribution of different surface species. However, the utility of ToF-SIMS datasets may be limited by their large size, degraded mass resolution and low ion counts per pixel. Through denoising and multivariate image analysis, regions of similar chemistries may be differentiated more readily in ToF-SIMS image data. Three established denoising algorithms -down-binning, boxcar and wavelet filtering -were applied to ToF-SIMS images of different surface geometries and chemistries. The effect of these filters on the performance of principal component analysis (PCA) was evaluated in terms of the capture of important chemical image features in the principal component score images, the quality of the principal component score images and the ability of the principal components to explain the chemistries responsible for the image contrast. All filtering methods were found to improve the performance of PCA for all image datasets studied by improving capture of image features and producing principal component score images of higher quality than the unfiltered ion images. The loadings for filtered and unfiltered PCA models described the regions of chemical contrast by identifying peaks defining the regions of different surface chemistry. Down-binning the images to increase pixel size and signal was the most effective technique to improve PCA performance.

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Section: Introductionmentioning

confidence: 99%

Denoising and multivariate analysis of time‐of‐flight SIMS images

Wickes

Kim

Castner

2003

Surface & Interface Analysis

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“…The wavelet transform is based on a function j with average value zero, which becomes zero out of a finite time domain [18]. This function is called the mother wavelet [10,[13][14][15]17]. Various mother wavelets with different properties have been generated so far [19].…”

Section: Data Denoisingmentioning

confidence: 99%

“…These filters have not always yielded satisfactory results [9]. Wavelet filtering is an alternative method to smooth data [10]. Previous investigations by Ismail and Asfour [9] have shown the superiority of wavelet filtering in human kinematic data compared with Butterworth digital filtering.…”

Section: Introductionmentioning

confidence: 98%

“…The wavelet transform displaces the wavelet and the scaling function along the time axis of the signal and scales these functions for the best fit to the signal at that specific time. Consequently, a better adaptation to complicated nonperiodic signal structures, such as abrupt changes or trends, can be efficiently captured by the wavelet transform [10,13]. Therefore, low-frequency signals as well as highfrequency signals can be illustrated at high resolution [13][14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

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Application of Wavelet Filtering to Analyze Acceleration-Time Curves of Horses Trotted on Different Surfaces

Kruse

Salau

Traulsen

et al. 2012

Journal of Equine Veterinary Science

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“…After applying the different filtering algorithms, a quantitative evaluation was carried out using the peak signal-to-noise ratio (PSNR) as figure of merit. This figure of merit has proved to be useful not only to measure the quality of video image processing but also for scientific analysis (Wolkenstein et al, 1997b). The PSNR can be derived from the mean square error (MSE).…”

Section: Figures Of Meritmentioning

confidence: 99%

Compression of Secondary Ion Microscopy Image Sets Using a Three-dimensional Wavelet Transformation

Wolkenstein

Hutter

2000

Microsc. Microanal.

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This article proposes a lossy three-dimensional (3-D) image compression method for 3-D secondary ion microscopy (SIMS) image sets that uses a separable nonuniform 3-D wavelet transform. A typical 3-D SIMS measurement produces relatively large amounts of data which has to be reduced for archivation purposes. Although it is possible to compress an image set slice by slice, more efficient compression can be achieved by exploring the correlation between slices. Compared to different two-dimensional (2-D) image compression methods, compression ratios of the 3-D wavelet method are about four times higher at a comparable peak signal-to-noise ratio (PSNR).

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Wavelet filtering for analytical data

Cited by 10 publications

References 0 publications

Denoising and multivariate analysis of time‐of‐flight SIMS images

Denoising and multivariate analysis of time‐of‐flight SIMS images

Application of Wavelet Filtering to Analyze Acceleration-Time Curves of Horses Trotted on Different Surfaces

Compression of Secondary Ion Microscopy Image Sets Using a Three-dimensional Wavelet Transformation

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