Synthetic aperture sonar (SAS) measures a scene from multiple views in order to increase the resolution of reconstructed imagery. Image reconstruction methods for SAS coherently combine measurements to focus acoustic energy onto the scene. However, image formation is typically under-constrained due to a limited number of measurements and bandlimited hardware, which limits the capabilities of existing reconstruction methods. To help meet these challenges, we design an analysis-by-synthesis optimization that leverages recent advances in neural rendering to perform coherent SAS imaging. Our optimization enables us to incorporate physics-based constraints and scene priors into the image formation process. We validate our method on simulation and experimental results captured in both air and water. We demonstrate both quantitatively and qualitatively that our method typically produces superior reconstructions than existing approaches. We share code and data for reproducibility.

翻译：合成孔径声纳（SAS）通过从多个视角测量场景来提高重建图像的分辨率。相干SAS图像重建方法通过相干组合测量结果，将声能量聚焦到场景上。然而，由于测量数量有限且硬件带宽受限，图像形成过程通常存在约束不足的问题，这限制了现有重建方法的能力。为应对这些挑战，我们设计了一种分析-合成优化方法，利用神经渲染的最新进展实现相干SAS成像。该优化使我们能够将基于物理的约束和场景先验融入图像形成过程。我们在空气和水中采集的仿真与实验结果上验证了该方法。定量和定性结果表明，我们的方法通常能产生优于现有方法的重建效果。我们公开代码和数据以确保结果可复现。