  In a previous work we have demonstrated a novel numerical model for the point
spread function (PSF) of an optical system that can efficiently model both
experimental measurements and lens design simulations of the PSF. The novelty
lies in the portability and the parameterization of this model, which allows
for completely new ways to validate optical systems, which is especially
interesting for mass production optics like in the automotive industry, but
also for ophtalmology. The numerical basis for this model is a non-linear
regression of the PSF with an artificial neural network (ANN). In this work we
examine two important aspects of this model: the spatial resolution and the
accuracy of the model. Measurement and simulation of a PSF can have a much
higher resolution then the typical pixel size used in current camera sensors,
especially those for the automotive industry. We discuss the influence this has
on on the topology of the ANN and the final application where the modeled PSF
is actually used. Another important influence on the accuracy of the trained
ANN is the error metric which is used during training. The PSF is a distinctly
non-linear function, which varies strongly over field and defocus, but
nonetheless exhibits strong symmetries and spatial relations. Therefore we
examine different distance and similarity measures and discuss its influence on
the modeling performance of the ANN.
