  Synthetic aperture sonar (SAS) image resolution is constrained by waveform
bandwidth and array geometry. Specifically, the waveform bandwidth determines a
point spread function (PSF) that blurs the locations of point scatterers in the
scene. In theory, deconvolving the reconstructed SAS image with the scene PSF
restores the original distribution of scatterers and yields sharper
reconstructions. However, deconvolution is an ill-posed operation that is
highly sensitive to noise. In this work, we leverage implicit neural
representations (INRs), shown to be strong priors for the natural image space,
to deconvolve SAS images. Importantly, our method does not require training
data, as we perform our deconvolution through an analysis-bysynthesis
optimization in a self-supervised fashion. We validate our method on simulated
SAS data created with a point scattering model and real data captured with an
in-air circular SAS. This work is an important first step towards applying
neural networks for SAS image deconvolution.
