Keywords: Distribution Shift, MTF, chromatic aberration, corruption, Deep Neural Networks
Abstract: Image classification is a long-standing task in computer vision with deep neural networks (DNN) producing excellent results on various challenges. However, they are required not only to perform highly accurate on benchmarks such as ImageNet, but also to robustly handle images in adverse conditions, such as modified lighting, sharpness, weather conditions and image compression. Various benchmarks aimed to measure robustness show that neural networks perform differently well under distribution shifts. While datasets such as ImageNet-C model for example common corruptions such as blur and adverse weather conditions, we argue that the properties of the optical system and the potentially resulting complex lens blur are insufficiently well studied in the literature. This study evaluates the impact of realistic optical corruptions on the ImageNet classification. The proposed complex corruption kernels are direction and wavelength dependent and include chromatic aberration, which are all to be expected in realistic scenarios such as autonomous driving applications. Our experiments on twelve different DNN models show significant differences of more than 5% in the top1 classification accuracy, when compared to the model performances on matched ImageNet-C blur kernels.