Astronomical telescopes suffer from a tradeoff between field of view (FoV) and image resolution: increasing the FoV leads to an optical field that is under-sampled by the science camera. This work presents a novel computational imaging approach to overcome this tradeoff by leveraging the existing adaptive optics (AO) systems in modern ground-based telescopes. Our key idea is to use the AO system's deformable mirror to apply a series of learned, precisely controlled distortions to the optical wavefront, producing a sequence of images that exhibit distinct, high-frequency, sub-pixel shifts. These images can then be jointly upsampled to yield the final super-resolved image. Crucially, we show this can be done while simultaneously maintaining the core AO operation--correcting for the unknown and rapidly changing wavefront distortions caused by Earth's atmosphere. To achieve this, we incorporate end-to-end optimization of both the induced mirror distortions and the upsampling algorithm, such that telescope-specific optics and temporal statistics of atmospheric wavefront distortions are accounted for. Our experimental results with a hardware prototype, as well as simulations, demonstrate significant SNR improvements of up to 12 dB over non-AO super-resolution baselines, using only existing telescope optics and no hardware modifications. Moreover, by using a precise bench-top replica of a complete telescope and AO system, we show that our methodology can be readily transferred to an operational telescope. Project webpage: https://www.cs.toronto.edu/~robin/aosr/

翻译：天文望远镜在视场（FoV）与图像分辨率之间存在权衡：扩大视场会导致科学相机对光场采样不足。本研究提出一种新颖的计算成像方法，通过利用现代地基望远镜中已有的自适应光学（AO）系统来克服这一权衡。我们的核心思想是利用AO系统的可变形反射镜施加一系列经学习、精确控制的光学波前畸变，从而产生一系列具有明显高频亚像素偏移的图像。这些图像可被联合上采样以生成最终的超分辨率图像。关键的是，我们证明这一过程可与AO系统的核心操作——校正由地球大气引起的未知且快速变化的波前畸变——同步进行。为实现此目标，我们采用了对诱导反射镜畸变与上采样算法的端到端联合优化，从而将望远镜特定光学特性及大气波前畸变的时间统计特性纳入考量。通过硬件原型实验与仿真，我们的结果表明：仅利用现有望远镜光学元件且无需硬件改造，相较于非AO超分辨率基线方法可获得高达12 dB的显著信噪比提升。此外，通过使用完整望远镜与AO系统的精确桌面复现装置，我们验证了该方法可直接迁移至运行中的望远镜系统。项目网页：https://www.cs.toronto.edu/~robin/aosr/