{"ID":2885911,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.04648","arxiv_id":"2508.04648","title":"Super Resolved Imaging with Adaptive Optics","abstract":"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/","short_abstract":"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) s...","url_abs":"https://arxiv.org/abs/2508.04648","url_pdf":"https://arxiv.org/pdf/2508.04648v1","authors":"[\"Robin Swanson\",\"Esther Y. H. Lin\",\"Masen Lamb\",\"Suresh Sivanandam\",\"Kiriakos N. Kutulakos\"]","published":"2025-08-06T17:15:48Z","proceeding":"astro-ph.IM","tasks":"[\"astro-ph.IM\",\"cs.CV\"]","methods":"[]","project_urls":"[\"https://www.cs.toronto.edu/~robin/aosr/\"]","has_code":false,"code_links":[{"ID":611255,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2885911,"paper_url":"https://arxiv.org/abs/2508.04648","paper_title":"Super Resolved Imaging with Adaptive Optics","repo_url":"https://github.com/swansonr/AOSR","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}