Progressive Diffusion Autofocus for Synthetic Aperture Sonar Imagery

Autofocus algorithms for synthetic aperture sonar (SAS) enhance the autonomy of uncrewed underwater vehicles by counteracting environmental factors that often compromise image quality, thus preserving the vehicles' capacity for accurate perception. Often, these errors are systematic resulting from misestimation of environmental parameters (e.g., sound speed) or sonar position. Traditional autofocusing techniques aimed at correcting these distortions may result in degrading well-focused imagery when applied repeatedly, while single-attempt focusing methods may not fully correct the errors. In this work, we introduce a novel SAS autofocus approach named Progressive Diffusion Autofocus (PDA), which incrementally refines the focus over several iterations, drawing inspiration from recent deep-learning-centric diffusion processes. Our method iteratively improves focus with each iteration, thereby enhancing the image progressively. We evaluate our approach against established methods, such as optimization-based sharpness metrics and a recently introduced deep learning method, Deep Adaptive Phase Learning (DAPL). Our results indicate that our PDA method not only consistently improves image quality, but also minimizes the degradation of images that are already well-focused.