Noise suppression in scanning Auger Images ? comparison of various digital filters

Böhmig, S. D.; Beilschmidt, Herbert; Reichl, B. M.

doi:10.1007/bf00321412

Cited by 5 publications

(1 citation statement)

References 6 publications

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“…And finally, for ELEE, HMF, SNN, Kuwahara, and the adaptive Wiener filters, a window size of 5 × 5 pixels was used in the numerical evaluation of these filters. This choice of window size for these filters is also commonly made in the literature [24][25][26][27][28][29][30][31][32][33][34][35][41][42][43][44][45]. On the other hand, for an OCT image to be filtered, the optimum choice of this window size should also take into account the residual correlation between two adjacent pixels of the OCT image.…”

Section: Introductionmentioning

confidence: 99%

Speckle reduction in optical coherence tomography images using digital filtering

et al. 2007

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Speckle noise is a ubiquitous artifact that limits the interpretation of optical coherence tomography images. Here we apply various speckle-reduction digital filters to optical coherence tomography images and compare their performance. Our results indicate that shift-invariant, nonorthogonal wavelet-transform-based filters together with enhanced Lee and adaptive Wiener filters can significantly reduce speckle and increase the signal-to-noise ratio, while preserving strong edges. The speckle reduction capabilities of these filters are also compared with speckle reduction from incoherent angular compounding. Our results suggest that by using these digital filters, the number of individual angles required to attain a certain level of speckle reduction can be decreased.

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Section: Introductionmentioning

confidence: 99%

Speckle reduction in optical coherence tomography images using digital filtering

et al. 2007

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Automatic matching of dissimilar SAM‐images

Böhmig

Reichl

Eisl

et al. 1994

Surface & Interface Analysis

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An important step in analysing multispectral image data-measured at different times or at slightly differing positions of the sample-is the matching of these data. The matching process consists of two steps: registration, which restores image differences induced by varying detector positions, and comparison, which follows image analysis and makes it possible to overlap different images and to detect and classify differences of the image contents. ' We present a new technique for registering uni-and multispectral image data, which is independent of the underlying grey-level distributions. This characteristic property is achieved by first transforming the representation of the image into a set of connected regions with homogeneous grey levels by means of a k-means classification algorithm. The idea is to compare the geometric extensions of overlapping regions instead of single pixel values of images for a given geometric transformation. The transformation which yields the best similarity is then chosen to restore the dataThe method was applied to several different uni-and multispectral scanning Auger microscopy (SAM) images and the results were judged according to accuracy and robustness.

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Segmentation and scatter diagram analysis of scanning Auger images ? A critical comparison of results

Böhmig

Reichl

1993

Fresenius J Anal Chem

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Noise suppression in scanning Auger Images ? comparison of various digital filters

Cited by 5 publications

References 6 publications

Speckle reduction in optical coherence tomography images using digital filtering

Speckle reduction in optical coherence tomography images using digital filtering

Automatic matching of dissimilar SAM‐images

Segmentation and scatter diagram analysis of scanning Auger images ? A critical comparison of results

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