@article{WittpahlZakourLehmannetal.2018, author = {Wittpahl, Christian and Zakour, Hatem Ben and Lehmann, Matthias and Braun, Alexander}, title = {Realistic Image Degradation with Measured PSF}, series = {Electronic Imaging, Autonomous Vehicles and Machines 2018}, journal = {Electronic Imaging, Autonomous Vehicles and Machines 2018}, number = {17}, publisher = {Society for Imaging Science and Technology}, issn = {2470-1173}, doi = {10.2352/ISSN.2470-1173.2018.17.AVM-149}, pages = {149}, year = {2018}, language = {en} } @article{LehmannWittpahlZakouretal.2019, author = {Lehmann, Matthias and Wittpahl, Christian and Zakour, Hatem Ben and Braun, Alexander}, title = {Resolution and accuracy of nonlinear regression of point spread function with artificial neural networks}, series = {Optical Engineering}, volume = {58}, journal = {Optical Engineering}, number = {4}, publisher = {SPIE}, issn = {0091-3286}, doi = {10.1117/1.oe.58.4.045101}, pages = {045101}, year = {2019}, abstract = {We had already demonstrated a numerical model for the point spread function (PSF) of an optical system that can efficiently model both the experimental measurements and the lens design simulations of the PSF. The novelty lies in the portability and the parameterization of this model, which allow for completely new ways to validate optical systems, which is especially interesting not only for mass production optics such as in the automotive industry but also for ophthalmology. The numerical basis for this model is a nonlinear regression of the PSF with an artificial neural network (ANN). After briefly describing both the principle and the applications of the model, we then discuss two optically important aspects: the spatial resolution and the accuracy of the model. Using mean squared error (MSE) as a metric, we vary the topology of the neural network, both in the number of neurons and in the number of hidden layers. Measurement and simulation of a PSF can have a much higher spatial resolution than the typical pixel size used in current camera sensors. We discuss the influence this has on the topology of the ANN. The relative accuracy of the averaged pixel MSE is below 10  -  4, thus giving confidence that the regression does indeed model the measurement data with good accuracy. This article is only the starting point, and we propose several research avenues for future work.}, language = {en} } @article{LehmannWittpahlZakouretal.2019, author = {Lehmann, Matthias and Wittpahl, Christian and Zakour, Hatem Ben and Braun, Alexander}, title = {Modeling realistic optical aberrations to reuse existing drive scene recordings for autonomous driving validation}, series = {Journal of Electronic Imaging}, volume = {28}, journal = {Journal of Electronic Imaging}, number = {1}, publisher = {SPIE}, issn = {1560-229X}, doi = {10.1117/1.JEI.28.1.013005}, pages = {013005}, year = {2019}, abstract = {Training autonomous vehicles requires lots of driving sequences in all situations. Collecting and labeling these drive scenes is a very time-consuming and expensive process. Currently, it is not possible to reuse these drive scenes with different optical properties, because there exists no numerically efficient model for the transfer function of the optical system. We present a 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 numerical basis for this model is a nonlinear regression of the PSF with an artificial neural network. The novelty lies in the portability and the parameterization of this model. We present a lens measurement series, yielding a numerical function for the PSF that depends only on the parameters defocus, field, and azimuth. By convolving existing images and videos with this PSF, we generate images as if seen through the measured lens. The methodology applies to any optical scenario, but we focus on the context of autonomous driving, where the quality of the detection algorithms depends directly on the optical quality of the used camera system. With this model, it is possible to reuse existing recordings, with the potential to avoid millions of test drive miles. The parameterization of the optical model allows for a method to validate the functional and safety limits of camera-based advanced driver assistance systems based on the real, measured lens actually used in the product.}, language = {en} } @inproceedings{LehmannWittpahlZakouretal.2018, author = {Lehmann, Matthias and Wittpahl, Christian and Zakour, Hatem Ben and Braun, Alexander}, title = {Resolution and accuracy of non-linear regression of PSF with artificial neural networks}, series = {SPIE Optical Systems Design: Optical Instrument Science, Technology, and Applications, 2018, Frankfurt, Germany}, volume = {Proc. SPIE, Vol. 10695}, booktitle = {SPIE Optical Systems Design: Optical Instrument Science, Technology, and Applications, 2018, Frankfurt, Germany}, number = {106950C}, editor = {Haverkamp, Nils and Youngworth, Richard N.}, publisher = {International Society for Optics and Photonics}, address = {Frankfurt}, organization = {International Society for Optics and Photonics}, doi = {10.1117/12.2313144}, pages = {52 -- 63}, year = {2018}, abstract = {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.}, language = {en} }