Image denoising

Synonyms
Noise reduction
Description

Outline The incessant development of improved microscopy imaging techniques, as well as the advent of highly selective fluorescent dyes has made possible the precise identification of tagged molecules in almost any biological specimen. Of particular interest are the visualization and the study of living cells, which induce tight constraints on the imaging process. To avoid the alteration of the sample and to achieve a high temporal resolution, low fluorophore concentrations, low-power illumination and short exposure time need to be used in practice. Such restrictions have a tremendous impact on the image quality. This is why we have recently introduced a new method, coined PURE-LET [1,2,3], for efficient, fast, and automatic denoising of multidimensional fluorescence microscopy images.

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Description

This Node implements the so-called anisotropic diffusion scheme of Perona and Malik, 1990. For details on the anisotropic diffusion principles, see: http://en.wikipedia.org/wiki/Anisotropic_diffusion, and the original paper: Perona and Malik. Scale-Space and Edge Detection Using Anisotropic Diffusion. IEEE Transactions on Pattern Analysis and Machine Intelligence (1990) vol. 12 pp. 629-639

The Options allows to use different Functions to be used for filtering.

This node is contained in KNIME Image Processing extension

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Description

Applies average filtering to images in n-dimensions

Description

This package implements the interscale orthonormal wavelet thresholding algorithm based on the SURE-LET principle. A multichannel extension is also available. Java Applets are available too.

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