Kalman filter for noisy movies 1.0

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).function imageStack=Kalman_Stack_Filter(imageStack,percentvar,gain) Purpose 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 Inputs 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. Output imageStack - the filtered image stack Note: The time series will look noisy at first then become smoother as the filter accumulates evidence. Rob Campbell, August 2009

Requirements:	No special requirements
Platforms:	Matlab
Keyword:	Higher,  Larger,  Lower,  Pixel,  Predicted,  Smoother,  Strength,  Weight
Users rating:	0/10