Run exampleFilter.m to see how the algorithm performs on a sample of moderately noisy 2-photon imaging data. This function is a faster, vectorised, version of Java code written by C.P. Mauer as part of an ImageJ plugin (see below).
Implements a predictive Kalman-like filter in the time domain of the image stack. Algorithm taken from Java code by C.P. Mauer. http://rsb.info.nih.gov/ij/plugins/kalman.html
imageStack - a 3d matrix comprising of a noisy image sequence. Time is
the 3rd dimension.
gain - the strength of the filter [0 to 1]. Larger gain values means more
aggressive filtering in time so a smoother function with a lower
peak. Gain values above 0.5 will weight the predicted value of the
pixel higher than the observed value.
percentvar - the initial estimate for the noise [0 to 1]. Doesn't have
much of an effect on the algorithm.
imageStack - the filtered image stack
The time series will look noisy at first then become smoother as the
filter accumulates evidence.
Rob Campbell, August 2009
Rob Campbell (2021). Kalman filter for noisy movies (https://www.mathworks.com/matlabcentral/fileexchange/26334-kalman-filter-for-noisy-movies), MATLAB Central File Exchange. Retrieved .
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