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Implementation of some image correlation spectroscopy tools

mahotas.convolve(f, weights, mode='reflect', cval=0.0, out={new array})

Convolution of f and weights

Convolution is performed in doubles to avoid over/underflow, but the result is then cast to f.dtype. This conversion may result in over/underflow when using small integer types or unsigned types (if the output is negative). Converting to a floating point representation avoids this issue:

c = convolve(f.astype(float), kernel)

This plugin can be used to add synthetic Poisson-distributed noise to an image in order to simulate shot noise of various signal-to-noise ratios. It can be used to generate benchmark images in order to assess the accuracy and robustness of image processing algorithms as a function of the noise level present in images.

ImarisReader is a set of classes for reading the data stored in ims files. ImarisReader can read the primary image data, as well as the data for segmented objects: Cells, Filaments, Spots and Surfaces.

Histogram-based background subtractor for ImageJ.

The implemented algorithm is based on the assumption that, compared to the background region, object (foreground) regions are small. The plugin builds local histograms and assumes the most occuring intensity to be part of the background.