  Circular Synthetic aperture sonars (CSAS) capture multiple observations of a
scene to reconstruct high-resolution images. We can characterize resolution by
modeling CSAS imaging as the convolution between a scene's underlying point
scattering distribution and a system-dependent point spread function (PSF). The
PSF is a function of the transmitted waveform's bandwidth and determines a
fixed degree of blurring on reconstructed imagery. In theory, deconvolution
overcomes bandwidth limitations by reversing the PSF-induced blur and
recovering the scene's scattering distribution. However, deconvolution is an
ill-posed inverse problem and sensitive to noise. We propose a self-supervised
pipeline (does not require training data) that leverages an implicit neural
representation (INR) for deconvolving CSAS images. We highlight the performance
of our SAS INR pipeline, which we call SINR, by implementing and comparing to
existing deconvolution methods. Additionally, prior SAS deconvolution methods
assume a spatially-invariant PSF, which we demonstrate yields subpar
performance in practice. We provide theory and methods to account for a
spatially-varying CSAS PSF, and demonstrate that doing so enables SINR to
achieve superior deconvolution performance on simulated and real acoustic SAS
data. We provide code to encourage reproducibility of research.
