28 May 2018 Resolution and accuracy of non-linear regression of PSF with artificial neural networks
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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.
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Matthias Lehmann, Matthias Lehmann, Christian Wittpahl, Christian Wittpahl, Hatem Ben Zakour, Hatem Ben Zakour, Alexander Braun, Alexander Braun, } "Resolution and accuracy of non-linear regression of PSF with artificial neural networks", Proc. SPIE 10695, Optical Instrument Science, Technology, and Applications, 106950C (28 May 2018); doi: 10.1117/12.2313144; https://doi.org/10.1117/12.2313144

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