  Computer vision using deep neural networks (DNNs) has brought about seminal
changes in people's lives. Applications range from automotive, face recognition
in the security industry, to industrial process monitoring. In some cases, DNNs
infer even in safety-critical situations. Therefore, for practical
applications, DNNs have to behave in a robust way to disturbances such as
noise, pixelation, or blur. Blur directly impacts the performance of DNNs,
which are often approximated as a disk-shaped kernel to model defocus. However,
optics suggests that there are different kernel shapes depending on wavelength
and location caused by optical aberrations. In practice, as the optical quality
of a lens decreases, such aberrations increase. This paper proposes
OpticsBench, a benchmark for investigating robustness to realistic, practically
relevant optical blur effects. Each corruption represents an optical aberration
(coma, astigmatism, spherical, trefoil) derived from Zernike Polynomials.
Experiments on ImageNet show that for a variety of different pre-trained DNNs,
the performance varies strongly compared to disk-shaped kernels, indicating the
necessity of considering realistic image degradations. In addition, we show on
ImageNet-100 with OpticsAugment that robustness can be increased by using
optical kernels as data augmentation. Compared to a conventionally trained
ResNeXt50, training with OpticsAugment achieves an average performance gain of
21.7% points on OpticsBench and 6.8% points on 2D common corruptions.
