  Synthetic aperture sonar (SAS) requires precise time-of-flight measurements
of the transmitted/received waveform to produce well-focused imagery. It is not
uncommon for errors in these measurements to be present resulting in image
defocusing. To overcome this, an \emph{autofocus} algorithm is employed as a
post-processing step after image reconstruction to improve image focus. A
particular class of these algorithms can be framed as a sharpness/contrast
metric-based optimization. To improve convergence, a hand-crafted weighting
function to remove "bad" areas of the image is sometimes applied to the
image-under-test before the optimization procedure. Additionally, dozens of
iterations are necessary for convergence which is a large compute burden for
low size, weight, and power (SWaP) systems. We propose a deep learning
technique to overcome these limitations and implicitly learn the weighting
function in a data-driven manner. Our proposed method, which we call Deep
Autofocus, uses features from the single-look-complex (SLC) to estimate the
phase correction which is applied in $k$-space. Furthermore, we train our
algorithm on batches of training imagery so that during deployment, only a
single iteration of our method is sufficient to autofocus. We show results
demonstrating the robustness of our technique by comparing our results to four
commonly used image sharpness metrics. Our results demonstrate Deep Autofocus
can produce imagery perceptually better than common iterative techniques but at
a lower computational cost. We conclude that Deep Autofocus can provide a more
favorable cost-quality trade-off than alternatives with significant potential
of future research.
