  In this work we explore the previously proposed approach of direct blind
deconvolution and denoising with convolutional neural networks in a situation
where the blur kernels are partially constrained. We focus on blurred images
from a real-life traffic surveillance system, on which we, for the first time,
demonstrate that neural networks trained on artificial data provide superior
reconstruction quality on real images compared to traditional blind
deconvolution methods. The training data is easy to obtain by blurring sharp
photos from a target system with a very rough approximation of the expected
blur kernels, thereby allowing custom CNNs to be trained for a specific
application (image content and blur range). Additionally, we evaluate the
behavior and limits of the CNNs with respect to blur direction range and
length.
