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Fitting a linescan image. **A** Each time trace in the raw fluorescence signal is fitted and a denoised image is obtained. **B** The denoised image is a sum of fitted events (**C**) and baseline (**D**). ΔF/F0 image (**E**) is obtained by dividing the event image (**C**) with the baseline (**D**). Because the fitted baseline is a function of time, the F_0 image is automatically corrected for temporal changes in background flourescence (e.g., bleaching). **F** The residual signal obtained when subtracting the fitted image from raw data. **G** Residual values are normally distributed.   diff --git a/figures/fit_linescan1/caption.md b/figures/fit_linescan1/caption.md  new file mode 100644  index 0000000..a8a1b0c  --- /dev/null  +++ b/figures/fit_linescan1/caption.md 
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\label{fig:fig1}  Fitting function. **A**: Fitting function for transients given by eq 1. **B** Result of fitting raw fluorescence data with the combined fitting function. **C** Raw signal is the sum of the fit, noise (W) and residual (R). **D** The fit is a superposition of the baseline and pixel events.  diff --git a/figures/fitting2/caption.md b/figures/fitting2/caption.md  new file mode 100644  index 0000000..a8a1b0c  --- /dev/null  +++ b/figures/fitting2/caption.md 
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\end{array}  \right)  \]  , where \(E^s\) and \(E^p\) are shape and position submatrices, respectively, containing event shape and position parameters for all events from all pixels and making up the entire image event matrix \(E\). The separation of shape and position paramters parameters  for events is necessary in the next clustering step.