  Synthetic aperture sonar (SAS) requires precise positional and environmental
information to produce well-focused output during the image reconstruction
step. However, errors in these measurements are commonly present resulting in
defocused imagery. To overcome these issues, an \emph{autofocus} algorithm is
employed as a post-processing step after image reconstruction for the purpose
of improving image quality using the image content itself. These algorithms are
usually iterative and metric-based in that they seek to optimize an image
sharpness metric. In this letter, we demonstrate the potential of machine
learning, specifically deep learning, to address the autofocus problem. We
formulate the problem as a self-supervised, phase error estimation task using a
deep network we call Deep Autofocus. Our formulation has the advantages of
being non-iterative (and thus fast) and not requiring ground truth
focused-defocused images pairs as often required by other deblurring deep
learning methods. We compare our technique against a set of common sharpness
metrics optimized using gradient descent over a real-world dataset. Our results
demonstrate Deep Autofocus can produce imagery that is perceptually as good as
benchmark iterative techniques but at a substantially lower computational cost.
We conclude that our proposed Deep Autofocus can provide a more favorable
cost-quality trade-off than state-of-the-art alternatives with significant
potential of future research.
