用自适应光学系统实现无需硬件改造的超分辨率成像。
Super Resolved Imaging with Adaptive Optics
- 利用自适应光学镜面施加可学习的微扰，生成带高频亚像素位移的图像序列。
- 实测与仿真均显示信噪比提升最高达12分贝，优于非自适应光学基线。
- 方法可直接部署于现有望远镜，无需额外硬件改动，适合天文观测场景。
地面望远镜面临视场（FoV）与图像分辨率之间的权衡：增大视场会导致科学相机对光学场采样不足。本文提出一种新型计算成像方法，利用现代地面望远镜已有的自适应光学（AO）系统克服该限制。核心思路是通过AO系统的变形镜对光波前施加一系列可学习、精确控制的畸变，生成具有独特高频亚像素位移的图像序列，进而联合上采样获得最终的超分辨率图像。关键在于，该方法可在同时保持核心AO功能——校正由地球大气引起的未知且快速变化的波前畸变——的前提下实现。为此，我们对诱导镜面畸变与上采样算法进行端到端联合优化，充分考虑望远镜特异性光学结构及大气波前畸变的时间统计特性。实验基于硬件原型及仿真验证，结果显示相比非自适应光学超分辨基线，信噪比最高提升12分贝，仅依赖现有望远镜光学系统且无需硬件修改。此外，通过使用完整望远镜与自适应光学系统的精密桌面复现平台，证明该方法可轻松迁移至实际运行望远镜。
原文摘要 · Abstract (English)
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/
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