Super Resolved Imaging with Adaptive Optics

• URL: http://arxiv.org/abs/2508.04648v1
• Date: Wed, 06 Aug 2025 17:15:48 GMT
• Title: Super Resolved Imaging with Adaptive Optics
• Authors: Robin Swanson, Esther Y. H. Lin, Masen Lamb, Suresh Sivanandam, Kiriakos N. Kutulakos,
• Abstract summary: Astronomical telescopes suffer from a tradeoff between field of view (FoV) and image resolution.<n>This work presents a novel computational imaging approach to overcome this tradeoff.<n>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.
• Score: 3.882292698785977
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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/

Related papers

• STAR: A Benchmark for Astronomical Star Fields Super-Resolution [51.79340280382437]
  We propose STAR, a large-scale astronomical SR dataset containing 54,738 flux-consistent star field image pairs.<n>We propose a Flux-Invariant Super Resolution (FISR) model that could accurately infer the flux-consistent high-resolution images from input photometry.
  arXiv  Detail & Related papers  (2025-07-22T09:28:28Z)
• From Stars to Molecules: AI Guided Device-Agnostic Super-Resolution Imaging [0.0]
  Super-resolution imaging has revolutionized the study of systems ranging from molecular structures to distant galaxies.<n>Existing super-resolution methods require extensive calibration and retraining for each imaging setup, limiting their practical deployment.<n>We introduce a device-agnostic deep-learning framework for super-resolution imaging of point-like emitters that eliminates the need for calibration data or explicit knowledge of optical system parameters.
  arXiv  Detail & Related papers  (2025-02-25T20:54:27Z)
• Exoplanet Detection via Differentiable Rendering [23.64604723151245]
  Direct imaging of exoplanets faces significant challenges due to the high contrast between host stars and their planets.<n>Wavefront aberrations introduce speckles in the telescope science images, which are patterns of diffracted starlight that can mimic the appearance of planets.<n>Traditional post-processing methods, operating primarily in the image intensity domain, do not integrate wavefront sensing data.<n>We present a differentiable rendering approach that leverages these wavefront sensing data to improve exoplanet detection.
  arXiv  Detail & Related papers  (2025-01-03T17:30:44Z)
• CRAYM: Neural Field Optimization via Camera RAY Matching [48.25100687172752]
  We introduce camera ray matching (CRAYM) into the joint optimization of camera poses and neural fields from multi-view images.<n>We formulate our per-ray optimization and matched ray coherence by focusing on camera rays passing through keypoints in the input images.
  arXiv  Detail & Related papers  (2024-12-02T15:39:09Z)
• Physics-Inspired Degradation Models for Hyperspectral Image Fusion [61.743696362028246]
  Most fusion methods solely focus on the fusion algorithm itself and overlook the degradation models. We propose physics-inspired degradation models (PIDM) to model the degradation of LR-HSI and HR-MSI. Our proposed PIDM can boost the fusion performance of existing fusion methods in practical scenarios.
  arXiv  Detail & Related papers  (2024-02-04T09:07:28Z)
• Adaptive Optical Imaging with Entangled Photons [0.0]
  Adaptive optics (AO) has revolutionized imaging in fields from astronomy to microscopy by correcting optical aberrations. Here, we propose an AO approach leveraging correlations between entangled photons to directly correct the point spread function (PSF) Our work improves AO for label-free microscopy and could play a major role in the development of quantum microscopes.
  arXiv  Detail & Related papers  (2023-08-22T14:37:30Z)
• Thin On-Sensor Nanophotonic Array Cameras [36.981384762023794]
  We introduce emphflat nanophotonic computational cameras as an alternative to commodity cameras. The optical array is embedded on a metasurface that, at 700nm height, is flat and sits on the sensor cover glass at 2.5mm focal distance from the sensor. We reconstruct a megapixel image from our flat imager with a emphlearned probabilistic reconstruction method that employs a generative diffusion model to sample an implicit prior.
  arXiv  Detail & Related papers  (2023-08-05T06:04:07Z)
• Optical Aberration Correction in Postprocessing using Imaging Simulation [17.331939025195478]
  The popularity of mobile photography continues to grow. Recent cameras have shifted some of these correction tasks from optical design to postprocessing systems. We propose a practical method for recovering the degradation caused by optical aberrations.
  arXiv  Detail & Related papers  (2023-05-10T03:20:39Z)
• Universal and Flexible Optical Aberration Correction Using Deep-Prior Based Deconvolution [51.274657266928315]
  We propose a PSF aware plug-and-play deep network, which takes the aberrant image and PSF map as input and produces the latent high quality version via incorporating lens-specific deep priors. Specifically, we pre-train a base model from a set of diverse lenses and then adapt it to a given lens by quickly refining the parameters.
  arXiv  Detail & Related papers  (2021-04-07T12:00:38Z)
• Single-shot Hyperspectral-Depth Imaging with Learned Diffractive Optics [72.9038524082252]
  We propose a compact single-shot monocular hyperspectral-depth (HS-D) imaging method. Our method uses a diffractive optical element (DOE), the point spread function of which changes with respect to both depth and spectrum. To facilitate learning the DOE, we present a first HS-D dataset by building a benchtop HS-D imager.
  arXiv  Detail & Related papers  (2020-09-01T14:19:35Z)
• Hyperspectral-Multispectral Image Fusion with Weighted LASSO [68.04032419397677]
  We propose an approach for fusing hyperspectral and multispectral images to provide high-quality hyperspectral output. We demonstrate that the proposed sparse fusion and reconstruction provides quantitatively superior results when compared to existing methods on publicly available images.
  arXiv  Detail & Related papers  (2020-03-15T23:07:56Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.