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\]  Convolving the image with this kind of kernel reduces the noise while retaining more of the original signal than simple averaging. Contributions from the i-th layer around the center will have the same weight as the central pixel. In this work we use the kernel with \(n=1\).   ### Region detection  The method works by fitting fluorescence signal from each pixel with a transient function. Prior to fitting, candidate regions containing possible events must be detected. This is achieved by modifying a continous wavelet transform based peak detection algorithm by Du et al.,\cite{Du_2006}. We have altered the method to also yield the width of the peak in addition to the location. Specific details of the region detection algorithm are given in the Supplementary Material.  Region estimation is performed iteratively. This is done to ensure overlapping events are correctly identified. At each pass regions which have no overlaps are fitted. After this the fit result is subtracted from the original signal and region detection is performed again, followed by fitting. This is done until no more regions are detected or successfully fitted.