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\subsection{FPN and centroiding accuracy}  - Result Photon counting  imagesand cross-sections  of bars Fig~\ref{fig_results} the USAF test chart are shown in Fig~\ref{fig_results}. As the photon events are relatively dim compared to the high camera background noise, the sum of the frames without any processing produces a noisy image (Fig~\ref{fig_results}a), while centroiding with one pixel accuracy removes camera the background and produces a clearer image (Fig~\ref{fig_results}b). Centroiding with sub-pixel resolution recovers the resolution lost by the electron diffusion in the sensor (Fig~\ref{fig_results}c,d).  - Pixel images (and maybe histograms) Fig~\ref{fig_pixelimages} The mismatch between the event shape and the centroiding function can lead to fixed pattern noise which can be seen as bright and dark stripes in the centroided image. The level of fixed pattern noise can be quantified by  \begin{equation}  FPN = \frac{N_{max} − N_{min}}{N_{mean}} \times 100\%,  \end{equation}  where $N_{max}$, $N_{min}$, and $N_{mean}$ are the maximum, minimum and average number of counts in the 5$\times$5 array of subpixel positions, respectively. For a completely random distribution, this number should approach zero with increasing number of photons.  - Table: Photon count statistics Several of ThunderSTORM's centroiding algorithms were tested to find an algorithm that leads to a minimum amount of fixed pattern noise. The distributions of centroided positions are shown in  Fig~\ref{fig_pixelimages}, where the pixels are further divided into a 13$\times$13 grid.  \textit{Discuss the centroiding results, FPN and bias caused by CCD readout.}