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Photon counting images of 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}b), while centroiding with one pixel accuracy removes the camera background and produces a clearer image (Fig~\ref{fig_results}c). Centroiding with 1/5-pixel accuracy (Fig~\ref{fig_results}d) seems to recover some of the resolution lost by the electron diffusion in the sensor, as shown in Fig~\ref{fig_results}e.  The mismatch between the photon  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. image.\cite{Suhling1999}  The level of fixed pattern noise can be quantified by \begin{equation}  FPN = \frac{N_{max} - N_{min}}{N_{mean}} \times 100\%,  \end{equation} 
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Several of ThunderSTORM's centroiding algorithms were tested to find an algorithm that leads to a minimum amount of fixed pattern noise (see Table~\ref{table:results}). The distributions of centroided positions are shown in Fig~\ref{fig_pixelimages}, where the pixels are further divided into a 13$\times$13 grid. Maximum likelihood (ML) fitting with a Gaussian PSF produces the most uniform distribution of localised positions (Fig~\ref{fig_pixelimages}a-c), as well as the finding the highest number of photons. As reported previously,\cite{Hirvonen2014_rsi} the horizontal widening of the photon events, most likely caused by the CCD read-out, causes a bias in the centroided positions and photons are most likely to be found towards the right edge of the pixel. Other methods produce results with similar distribution of centroided positions but with higher FPN and lower photon count (an example of weighted LS fit with integrated Gaussian PSF is shown in Fig~\ref{fig_pixelimages}d), with the exception of the radial symmetry method, which changes the bias to the vertical direction (Fig~\ref{fig_pixelimages}e).