Abstract
Quantitative fluorescent imaging requires optimization of the complete optical system, from the sample to the detector.
Such considerations are especially true for precision localization microscopy such as PALM and (d)STORM where the
precision of the result is limited by the noise in both the optical and detection systems. Here, we present a Camera
Simulation Engine (CSE) that allows comparison of imaging results from CCD, CMOS and EM-CCD cameras under
various sample conditions and can accurately validate the quality of precision localization algorithms and camera
performance. To achieve these results, the CSE incorporates the following parameters: 1) Sample conditions including
optical intensity, wavelength, optical signal shot noise, and optical background shot noise; 2) Camera specifications
including QE, pixel size, dark current, read noise, EM-CCD excess noise; 3) Camera operating conditions such as
exposure, binning and gain. A key feature of the CSE is that, from a single image (either real or simulated "ideal") we
generate a stack of statistically realistic images. We have used the CSE to validate experimental data showing that
certain current scientific CMOS technology outperforms EM-CCD in most super-resolution scenarios. Our results
support using the CSE to efficiently and methodically select cameras for quantitative imaging applications. Furthermore,
the CSE can be used to robustly compare and evaluate new algorithms for data analysis and image reconstruction. These
uses of the CSE are particularly relevant to super-resolution precision localization microscopy and provide a faster,
simpler and more cost effective means of system optimization, especially camera selection.
