  State-of-the-art algorithms for many semantic visual tasks are based on the
use of convolutional neural networks. These networks are commonly trained, and
evaluated, on large annotated datasets of artifact-free high-quality images. In
this paper, we investigate the effect of one such artifact that is quite common
in natural capture settings: optical blur. We show that standard network
models, trained only on high-quality images, suffer a significant degradation
in performance when applied to those degraded by blur due to defocus, or
subject or camera motion. We investigate the extent to which this degradation
is due to the mismatch between training and input image statistics.
Specifically, we find that fine-tuning a pre-trained model with blurred images
added to the training set allows it to regain much of the lost accuracy. We
also show that there is a fair amount of generalization between different
degrees and types of blur, which implies that a single network model can be
used robustly for recognition when the nature of the blur in the input is
unknown. We find that this robustness arises as a result of these models
learning to generate blur invariant representations in their hidden layers. Our
findings provide useful insights towards developing vision systems that can
perform reliably on real world images affected by blur.
