Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Adaptive optics for high-resolution imaging


Adaptive optics (AO) is a technique that corrects for optical aberrations. It was originally proposed to correct for the blurring effect of atmospheric turbulence on images in ground-based telescopes and was instrumental in the work that resulted in the Nobel prize-winning discovery of a supermassive compact object at the centre of our galaxy. When AO is used to correct for the eye’s imperfect optics, retinal changes at the cellular level can be detected, allowing us to study the operation of the visual system and to assess ocular health in the microscopic domain. By correcting for sample-induced blur in microscopy, AO has pushed the boundaries of imaging in thick tissue specimens, such as when observing neuronal processes in the brain. In this primer, we focus on the application of AO for high-resolution imaging in astronomy, vision science and microscopy. We begin with an overview of the general principles of AO and its main components, which include methods to measure the aberrations, devices for aberration correction, and how these components are linked in operation. We present results and applications from each field along with reproducibility considerations and limitations. Finally, we discuss future directions.

This is a preview of subscription content

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Fig. 1: The nature and effect of wavefront aberrations and how they are corrected.
Fig. 2: Modal representation of aberrations using Zernike polynomials according to the Noll notation35.
Fig. 3: Principles of the Shack–Hartmann wavefront sensor and pyramid wavefront sensor.
Fig. 4: Indirect sensing schemes.
Fig. 5: Three main types of corrector.
Fig. 6: Influence functions and dynamic control.
Fig. 7: Image improvements from astronomical AO systems.
Fig. 8: AO performance on a subject with high myopia.
Fig. 9: AO in optical microscopy.
Fig. 10: AO in astronomy.
Fig. 11: Cellular-level imaging in the living human retina using different AO imaging methods.
Fig. 12: High-resolution optical microscopy with AO.


  1. 1.

    Booth, M. J. Adaptive optical microscopy: the ongoing quest for a perfect image. Light. Sci. Appl. 3, e165 (2014).

    ADS  Google Scholar 

  2. 2.

    Ji, N. Adaptive optical fluorescence microscopy. Nat. Methods 14, 374–380 (2017).

    Google Scholar 

  3. 3.

    Beckers, J. M. Adaptive optics for astronomy: principles, performance, and applications. Annu. Rev. Astron. Astr. 31, 13–62 (1993).

    ADS  MathSciNet  Google Scholar 

  4. 4.

    Porter, J., Queener, H. M., Lin, J. E., Thorn, K. & Awwal, A. Adaptive Optics for Vision Science: Principles, Practices, Design and Applications (Wiley, 2006).

  5. 5.

    Kubby, J., Gigan, S. & Cui, M. Adaptive Optical Microscopy for Biological Imaging (Cambridge Univ. Press, 2019).

  6. 6.

    Roddier, F. Adaptive Optics in Astronomy (Cambridge Univ. Press, 1999).

  7. 7.

    Davies, R. & Kasper, M. Adaptive optics for astronomy. Annu. Rev. Astron. Astr. 50, 305–351 (2012).

    ADS  Google Scholar 

  8. 8.

    Ji, N., Sato, T. R. & Betzig, E. Characterization and adaptive optical correction of aberrations during in vivo imaging in the mouse cortex. Proc. Natl Acad. Sci. USA 109, 22–27 (2012).

    ADS  Google Scholar 

  9. 9.

    Liu, R., Li, Z., Marvin, J. S. & Kleinfeld, D. Direct wavefront sensing enables functional imaging of infragranular axons and spines. Nat. Methods 16, 615–618 (2019).

    Google Scholar 

  10. 10.

    Miller, D. T. & Kurokawa, K. Cellular scale imaging of transparent retinal structures and processes using adaptive optics optical coherence tomography. Annu. Rev. Vis. Sci. 6, 115–148 (2020).

    Google Scholar 

  11. 11.

    Burns, S. A., Elsner, A. E., Sapoznik, K. A., Warner, R. L. & Gast, T. J. Adaptive optics imaging of the human retina. Prog. Retin. Eye Res. 68, 1–30 (2019).

    Google Scholar 

  12. 12.

    Georgiou, M. et al. Adaptive optics imaging of inherited retinal diseases. Brit. J. Ophthalmol. 102, 1028 (2018).

    Google Scholar 

  13. 13.

    Roorda, A. & Duncan, J. L. Adaptive optics ophthalmoscopy. Annu. Rev. Vis. Sci. 1, 1–32 (2014).

    Google Scholar 

  14. 14.

    Gill, J. S., Moosajee, M. & Dubis, A. M. Cellular imaging of inherited retinal diseases using adaptive optics. Eye 33, 1683–1698 (2019).

    Google Scholar 

  15. 15.

    Babcock, H. W. The possibility of compensating astronomical seeing. Publ. Astron. Soc. Pac. 65, 229 (1953).

    ADS  Google Scholar 

  16. 16.

    Tyson, R. K. Principles of Adaptive Optics (CRC Press, 2015).

  17. 17.

    Vangindertael, J. et al. An introduction to optical super-resolution microscopy for the adventurous biologist. Methods Appl. Fluores. 6, 022003 (2018).

    ADS  Google Scholar 

  18. 18.

    Dai, Y. et al. Active compensation of extrinsic polarization errors using adaptive optics. Opt. Express 27, 35797–35810 (2019).

    ADS  Google Scholar 

  19. 19.

    He, C., Hu, Q., Dai, Y. & Booth, M. J. Vectorial adaptive optics - correction of polarization and phase. in Adaptive Optics and Wavefront Control for Biological Systems VI Vol. 11248 1124808 (OSA Publishing, 2020).

  20. 20.

    Felberer, F., Kroisamer, J.-S., Hitzenberger, C. K. & Pircher, M. Lens based adaptive optics scanning laser ophthalmoscope. Opt. Express 20, 17297–17310 (2012).

    ADS  Google Scholar 

  21. 21.

    Liu, Z., Kocaoglu, O. P. & Miller, D. T. In-the-plane design of an off-axis ophthalmic adaptive optics system using toroidal mirrors. Biomed. Opt. Express 4, 3007–3029 (2013).

    Google Scholar 

  22. 22.

    Young, L. K., Morris, T. J., Saunter, C. D. & Smithson, H. E. Compact, modular and in-plane AOSLO for high-resolution retinal imaging. Biomed. Opt. Express 9, 4275–4293 (2018).

    Google Scholar 

  23. 23.

    Thaung, J., Knutsson, P., Popovic, Z. & Owner-Petersen, M. Dual-conjugate adaptive optics for wide-field high-resolution retinal imaging. Opt. Express 17, 4454–4467 (2009).

    ADS  Google Scholar 

  24. 24.

    Hampson, K. M. et al. Closed-loop multiconjugate adaptive optics for microscopy. in Adaptive Optics and Wavefront Control for Biological Systems VI Vol. 11248 1124809 (OSA Publishing, 2020).

  25. 25.

    Rigaut, F. & Neichel, B. Multiconjugate adaptive optics for astronomy. Annu. Rev. Astron. Astr. 56, 277–314 (2018).

    ADS  Google Scholar 

  26. 26.

    Hardy, J. W. Adaptive Optics for Astronomical Telescopes (Oxford Univ. Press, 1998).

  27. 27.

    Bedggood, P., Daaboul, M., Ashman, R., Smith, G. & Metha, A. Characteristics of the human isoplanatic patch and implications for adaptive optics retinal imaging. J. Biomed. Opt. 13, 024008 (2008).

    ADS  Google Scholar 

  28. 28.

    Wang, K. et al. Direct wavefront sensing for high-resolution in vivo imaging in scattering tissue. Nat. Commun. 6, 7276 (2015).

    ADS  Google Scholar 

  29. 29.

    Wang, C. et al. Multiplexed aberration measurement for deep tissue imaging in vivo. Nat. Methods 11, 1037–1040 (2014).

    Google Scholar 

  30. 30.

    Wang, K. et al. Rapid adaptive optical recovery of optimal resolution over large volumes. Nat. Methods 11, 625–628 (2014).

    Google Scholar 

  31. 31.

    Lin, R., Kipreos, E. T., Zhu, J., Khang, C. H. & Kner, P. Subcellular three-dimensional imaging deep through multicellular thick samples by structured illumination microscopy and adaptive optics. Nat. Comm. 12, 3148 (2021).

    ADS  Google Scholar 

  32. 32.

    Mertz, J., Paudel, H. & Bifano, T. G. Field of view advantage of conjugate adaptive optics in microscopy applications. Appl. Opt. 54, 3498–3506 (2015).

    ADS  Google Scholar 

  33. 33.

    Wilson, R. N., Franza, F. & Noethe, L. Adaptive optics: I. A system for optimizing the optical quality and reducing the costs of large telescopes. J. Mod. Opt. 34, 485–509 (1987).

    ADS  Google Scholar 

  34. 34.

    Lakshminarayanan, V. & Fleck, A. Zernike polynomials: a guide. J. Mod. Optic. 58, 1678–1678 (2011).

    ADS  Google Scholar 

  35. 35.

    Noll, R. J. Zernike polynomials and atmospheric turbulence. J. Opt. Soc. Am. 66, 207–210 (1976).

    ADS  Google Scholar 

  36. 36.

    Hampson, K., Antonello, J., Lane, R. & Booth, M. Sensorless adaptive optics. Zenodo (2020).

    Article  Google Scholar 

  37. 37.

    Thibos, L. N. et al. Standards for reporting the optical aberrations of eyes. J. Refract. Surg. 18, S652–S660 (2002).

    Google Scholar 

  38. 38.

    Kolmogorov, A. N. Dissipation of energy in the locally isotropic turbulence. Proc. R. Soc. Lond. Math. Phys. Sci. 434, 15–17 (1991).

    ADS  MathSciNet  MATH  Google Scholar 

  39. 39.

    Kolmogorov, A. N. The local structure of turbulence in incompressible viscous fluid for very large Reynolds numbers. Proc. R. Soc. Lond. Math. Phys. Sci. 434, 9–13 (1991).

    ADS  MathSciNet  MATH  Google Scholar 

  40. 40.

    Thibos, L. N., Hong, X., Bradley, A. & Cheng, X. Statistical variation of aberration structure and image quality in a normal population of healthy eyes. J. Opt. Soc. Am. A 19, 2329 (2002).

    ADS  Google Scholar 

  41. 41.

    Devaney, N. et al. Correction of ocular and atmospheric wavefronts: a comparison of the performance of various deformable mirrors. Appl. Opt. 47, 6550 (2008).

    ADS  Google Scholar 

  42. 42.

    Cantalloube, F. et al. Wind-driven halo in high-contrast images. Astron. Astrophys. 638, A98 (2020).

    Google Scholar 

  43. 43.

    Males, J. R. & Guyon, O. Ground-based adaptive optics coronagraphic performance under closed-loop predictive control. J. Astron. Telesc. Instrum. Syst. 4, 019001 (2018).

    ADS  Google Scholar 

  44. 44.

    Conan, J.-M., Rousset, G. & Madec, P.-Y. Wave-front temporal spectra in high-resolution imaging through turbulence. J. Opt. Soc. Am. A 12, 1559–1570 (1995).

    ADS  Google Scholar 

  45. 45.

    Roddier, F., Roddier, D., Northcott, M. J., Graves, J. E. & McKenna, D. L. One-dimensional spectra of turbulence-induced Zernike aberrations: time-delay and isoplanicity error in partial adaptive compensation. J. Opt. Soc. Am. A 10, 957–965 (1993).

    ADS  Google Scholar 

  46. 46.

    Salmon, T. O. & van de Pol, C. Normal-eye Zernike coefficients and root-mean-square wavefront errors. J. Cataract. Refract. Surg. 32, 2064–2074 (2006).

    Google Scholar 

  47. 47.

    Hofer, H., Artal, P., Singer, B., Aragón, J. L. & Williams, D. R. Dynamics of the eye’s wave aberration. J. Opt. Soc. Am. A 18, 497 (2001).

    ADS  Google Scholar 

  48. 48.

    Diaz-Santana, L., Torti, C., Munro, I., Gasson, P. & Dainty, C. Benefit of higher closed-loop bandwidths in ocular adaptive optics. Opt. Express 11, 2597–2605 (2003).

    ADS  Google Scholar 

  49. 49.

    Jarosz, J. et al. High temporal resolution aberrometry in a 50-eye population and implications for adaptive optics error budget. Biomed. Opt. Express 8, 2088–2105 (2017).

    Google Scholar 

  50. 50.

    Schmitt, J. M. & Kumar, G. Turbulent nature of refractive-index variations in biological tissue. Opt. Lett. 21, 1310–1312 (1996).

    ADS  Google Scholar 

  51. 51.

    Porter, J., Guirao, A., Cox, I. G. & Williams, D. R. Monochromatic aberrations of the human eye in a large population. J. Opt. Soc. Am. A 18, 1793–1803 (2001).

    ADS  Google Scholar 

  52. 52.

    Verstraete, H. R. G. W. et al. Wavefront sensorless adaptive optics OCT with the DONE algorithm for in vivo human retinal imaging [Invited]. Biomed. Opt. Express 8, 2261–2275 (2017).

    Google Scholar 

  53. 53.

    Shack, R. V. & Platt, B. C. Production and use of a lenticular Hartmann screen. J. Opt. Soc. Am. 61, 656–660 (1971).

    Google Scholar 

  54. 54.

    Thomas, S. et al. Comparison of centroid computation algorithms in a Shack–Hartmann sensor. Mon. Not. R. Astron. Soc. 371, 323–336 (2006).

    ADS  Google Scholar 

  55. 55.

    Geng, Y. et al. Optical properties of the mouse eye. Biomed. Opt. Express 2, 717–738 (2011).

    Google Scholar 

  56. 56.

    Akondi, V. & Dubra, A. Multi-layer Shack-Hartmann wavefront sensing in the point source regime. Biomed. Opt. Express 12, 409–432 (2021).

    Google Scholar 

  57. 57.

    Rahman, S. A. & Booth, M. J. Direct wavefront sensing in adaptive optical microscopy using backscattered light. Appl. Opt. 52, 5523–5532 (2013).

    ADS  Google Scholar 

  58. 58.

    Poyneer, L. A. Scene-based Shack-Hartmann wave-front sensing: analysis and simulation. Appl. Opt. 42, 5807–5815 (2003).

    ADS  Google Scholar 

  59. 59.

    Ashida, Y. et al. Imaging performance of microscopy adaptive-optics system using scene-based wavefront sensing. J. Biomed. Opt. 25, 123707 (2020).

    ADS  Google Scholar 

  60. 60.

    Tatulli, E. & Ramaprakash, A. N. Laser tomography adaptive optics: a performance study. J. Opt. Soc. Am. A 30, 2482 (2013).

    ADS  Google Scholar 

  61. 61.

    Laslandes, M., Salas, M., Hitzenberger, C. K. & Pircher, M. Influence of wave-front sampling in adaptive optics retinal imaging. Biomed. Opt. Express 8, 1183–1200 (2017).

    Google Scholar 

  62. 62.

    Ragazzoni, R. Pupil plane wavefront sensing with an oscillating prism. J. Mod. Opt. 43, 289–293 (1996).

    ADS  Google Scholar 

  63. 63.

    Engler, B., Weddell, S. & Clare, R. Wavefront sensing with prisms for astronomical imaging with adaptive optics. in 2017 International Conference on Image and Vision Computing New Zealand 1–7 (IEEE, 2017).

  64. 64.

    Chamot, S. R., Dainty, C. & Esposito, S. Adaptive optics for ophthalmic applications using a pyramid wavefront sensor. Opt. Express 14, 518–526 (2006).

    ADS  Google Scholar 

  65. 65.

    Iglesias, I. Pyramid phase microscopy. Opt. Lett. 36, 3636–3638 (2011).

    ADS  Google Scholar 

  66. 66.

    Berto, P., Rigneault, H. & Guillon, M. Wavefront sensing with a thin diffuser. Opt. Lett. 42, 5117–5120 (2017).

    ADS  Google Scholar 

  67. 67.

    Nishizaki, Y. et al. Deep learning wavefront sensing. Opt. Express 27, 240–251 (2019).

    ADS  Google Scholar 

  68. 68.

    Antonello, J., Barbotin, A., Chong, E. Z., Rittscher, J. & Booth, M. J. Multi-scale sensorless adaptive optics: application to stimulated emission depletion microscopy. Opt. Express 28, 16749–16763 (2020).

    ADS  Google Scholar 

  69. 69.

    Facomprez, A., Beaurepaire, E. & Débarre, D. Accuracy of correction in modal sensorless adaptive optics. Opt. Express 20, 2598–2612 (2012).

    ADS  Google Scholar 

  70. 70.

    Ji, N., Milkie, D. E. & Betzig, E. Adaptive optics via pupil segmentation for high-resolution imaging in biological tissues. Nat. Methods 7, 141–147 (2009).

    Google Scholar 

  71. 71.

    Milkie, D. E., Betzig, E. & Ji, N. Pupil-segmentation-based adaptive optical microscopy with full-pupil illumination. Opt. Lett. 36, 4206–4208 (2011).

    ADS  Google Scholar 

  72. 72.

    Gonsalves, R. A. Phase retrieval and diversity in adaptive optics. Opt. Eng. 21, 215829 (1982).

    Google Scholar 

  73. 73.

    Turcotte, R. et al. Dynamic super-resolution structured illumination imaging in the living brain. Proc. Natl Acad. Sci. USA 116, 9586–9591 (2019).

    ADS  Google Scholar 

  74. 74.

    Sauvage, J.-F., Fusco, T., Rousset, G. & Petit, C. Calibration and precompensation of noncommon path aberrations for extreme adaptive optics. J. Opt. Soc. Am. A 24, 2334–2346 (2007).

    ADS  Google Scholar 

  75. 75.

    Maurer, C., Jesacher, A., Bernet, S. & Ritsch-Marte, M. What spatial light modulators can do for optical microscopy. Laser Photonics Rev. 5, 81–101 (2011).

    ADS  Google Scholar 

  76. 76.

    Bonora, S. et al. Wavefront correction and high-resolution in vivo OCT imaging with an objective integrated multi-actuator adaptive lens. Opt. Express 23, 21931–21941 (2015).

    ADS  Google Scholar 

  77. 77.

    Banerjee, K., Rajaeipour, P., Ataman, Ç. & Zappe, H. Optofluidic adaptive optics. Appl. Opt. 57, 6338–6344 (2018).

    ADS  Google Scholar 

  78. 78.

    Doble, N., Miller, D. T., Yoon, G. & Williams, D. R. Requirements for discrete actuator and segmented wavefront correctors for aberration compensation in two large populations of human eyes. Appl. Opt. 46, 4501–4514 (2007).

    ADS  Google Scholar 

  79. 79.

    Guyon, O. Extreme adaptive optics. Annu. Rev. Astron. Astr. 56, 315–355 (2018).

    ADS  Google Scholar 

  80. 80.

    Duffner, R. W. The Adaptive Optics Revolution: A History (Univ. New Mexico Press, 2009).

  81. 81.

    Wizinowich, P. et al. First light adaptive optics images from the Keck II telescope: a new era of high angular resolution imagery. Publ. Astron. Soc. Pac. 112, 315–319 (2000).

    ADS  Google Scholar 

  82. 82.

    Lenzen, R. et al. NAOS-CONICA first on sky results in a variety of observing modes. in Instrument Design and Performance for Optical/Infrared Ground-based Telescopes Vol. 4841 944–952 (SPIE, 2003).

  83. 83.

    Rousset, G. et al. NAOS, the first AO system of the VLT: on-sky performance. Adaptive Optics Systems Technology II 4839, 140–149 (2003).

    ADS  Google Scholar 

  84. 84.

    Wizinowich, P. L. et al. The W. M. Keck observatory laser guide star adaptive optics system: overview. Publ. Astron. Soc. Pac. 118, 297–309 (2006).

    ADS  Google Scholar 

  85. 85.

    Johansson, E. M. et al. Upgrading the Keck AO wavefront controllers. in Adaptive Optics Systems Vol. 7015 70153E (SPIE, 2008).

  86. 86.

    van Dam, M. A. et al. The W. M. Keck observatory laser guide star adaptive optics system: performance characterization. Publ. Astron. Soc. Pac. 118, 310–318 (2006).

    ADS  Google Scholar 

  87. 87.

    Mawet, D. et al. Keck Planet Imager and Characterizer: concept and phased implementation. in Adaptive Optics Systems V Vol. 9909 99090D (SPIE, 2016).

  88. 88.

    Plantet, C. et al. Adaptive optics with an infrared pyramid wavefront sensor at Keck. J. Astron. Telesc. Instruments Syst. 6, 039003 (2020).

    ADS  Google Scholar 

  89. 89.

    Ragazzoni, R. & Farinato, J. Sensitivity of a pyramidic wave front sensor in closed loop adaptive optics. Astron. Astrophys. 350, L23–L26 (1999).

    ADS  Google Scholar 

  90. 90.

    Vérinaud, C. On the nature of the measurements provided by a pyramid wave-front sensor. Opt. Commun. 233, 27–38 (2004).

    ADS  Google Scholar 

  91. 91.

    Close, L. M. et al. Diffraction-limited visible light images of orion trapezium cluster with the magellan adaptive secondary AO system (MagAO). Astrophys. J. 774, 94 (2013).

    ADS  Google Scholar 

  92. 92.

    Wall, M. New telescope tech takes sharpest night sky photos ever. Space (2021).

  93. 93.

    d’Orgeville, C. et al. Gemini South multi-conjugate adaptive optics (GeMS) laser guide star facility on-sky performance results. in Adaptive Optics Systems III Vol. 8447 84471Q (SPIE, 2012).

  94. 94.

    Schmidt, D., Rimmele, T., Marino, J. & Wöger, F. A review of solar adaptive optics. in Adaptive Optics Systems V Vol. 9909 99090X (SPIE, 2016).

  95. 95.

    Johnson, L. C. et al. First light with adaptive optics: the performance of the DKIST high-order adaptive optics. in Adaptive Optics Systems VII Vol. 11448 114480T (SPIE, 2020).

  96. 96.

    Collins, G. P. Making stars to see stars: DOD adaptive optics work is declassified. Phys. Today 45, 17–21 (1992).

    Google Scholar 

  97. 97.

    Fugate, R. Q. The Starfire Optical Range 3.5-m adaptive optical telescope. in Large Ground-based Telescopes Vol. 4837 934–943 (SPIE, 2003).

  98. 98.

    Liang, J., Williams, D. R. & Miller, D. T. Supernormal vision and high-resolution retinal imaging through adaptive optics. J. Opt. Soc. Am. 14, 2884–2892 (1997).

    ADS  Google Scholar 

  99. 99.

    Hunter, J. J., Merigan, W. H. & Schallek, J. B. Imaging retinal activity in the living eye. Annu. Rev. Vis. Sci. 5, 15–45 (2019).

    Google Scholar 

  100. 100.

    Paques, M. et al. Adaptive optics ophthalmoscopy: Application to age-related macular degeneration and vascular diseases. Prog. Retin. Eye Res. 66, 1–16 (2018).

    Google Scholar 

  101. 101.

    Hampson, K. M. Introduction to Adaptive Optics for Vision Science (CRC Press, in the press).

  102. 102.

    Kocaoglu, O. P., Turner, T. L., Liu, Z. & Miller, D. T. Adaptive optics optical coherence tomography at 1 MHz. Biomed. Opt. Express 5, 4186–4200 (2014).

    Google Scholar 

  103. 103.

    Liu, Y. et al. High-speed adaptive optics for imaging the living human eye with optical coherence tomography. Invest. Ophthalmol. Vis. Sci. 61, 222 (2020).

    Google Scholar 

  104. 104.

    Gofas-Salas, E. et al. High loop rate adaptive optics flood illumination ophthalmoscope with structured illumination capability. Appl. Opt. 57, 5635–5642 (2018).

    ADS  Google Scholar 

  105. 105.

    Marcos, S. et al. Vision science and adaptive optics, the state of the field. Vis. Res. 132, 3–33 (2017).

    Google Scholar 

  106. 106.

    Li, K. Y., Mishra, S., Tiruveedhula, P. & Roorda, A. Comparison of control algorithms for a MEMS-based adaptive optics scanning laser ophthalmoscope. Proc. Am. Control. Conf. (2009).

    Article  Google Scholar 

  107. 107.

    Jonnal, R. S. CIAO: community inspired adaptive optics. Zenodo (2020).

    Article  Google Scholar 

  108. 108.

    ALPAO. ALPAO Core Engine. ALPAO (2020).

  109. 109.

    Imagine Eyes. WaveTuneTM. Imagine Eyes (2020).

  110. 110.

    Imagine Eyes. RTX1 Adaptive Optics Retinal Camera. Imagine Eyes (2020).

  111. 111.

    Boston Micromachines Corporation. The ApaerosTM AOSLO. Boston Micromachines Corporation (2020).

  112. 112.

    Physical Sciences Inc. Compact Adaptive Optics Retinal Imager. Physical Sciences Inc. (2020).

  113. 113.

    Booth, M. J., Neil, M. A. A., Juškaitis, R. & Wilson, T. Adaptive aberration correction in a confocal microscope. Proc. Natl Acad. Sci. USA 99, 5788–5792 (2002).

    ADS  Google Scholar 

  114. 114.

    Booth, M., Andrade, D., Burke, D., Patton, B. & Zurauskas, M. Aberrations and adaptive optics in super-resolution microscopy. Microscopy 64, 251–261 (2015).

    Google Scholar 

  115. 115.

    Denk, W., Strickler, J. & Webb, W. Two-photon laser scanning fluorescence microscopy. Science 248, 73–76 (1990).

    ADS  Google Scholar 

  116. 116.

    Chen, B.-C. et al. Lattice light-sheet microscopy: imaging molecules to embryos at high spatiotemporal resolution. Science 346, 1257998 (2014).

    Google Scholar 

  117. 117.

    Liu, T.-L. et al. Observing the cell in its native state: Imaging subcellular dynamics in multicellular organisms. Science 360, eaaq1392 (2018).

    ADS  Google Scholar 

  118. 118.

    Burke, D., Patton, B., Huang, F., Bewersdorf, J. & Booth, M. J. Adaptive optics correction of specimen-induced aberrations in single-molecule switching microscopy. Optica 2, 177–185 (2015).

    ADS  Google Scholar 

  119. 119.

    Melia, F. & Falcke, H. The supermassive black hole at the galactic center. Annu. Rev. Astron. Astr. 39, 309–352 (2001).

    ADS  Google Scholar 

  120. 120.

    Genzel, R. et al. The stellar cusp around the supermassive black hole in the galactic center. Astrophys. J. 594, 812–832 (2003).

    ADS  Google Scholar 

  121. 121.

    Ghez, A. M. et al. The first laser guide star adaptive optics observations of the Galactic Center: Sgr A*’s infrared color and the extended red emission in its vicinity. Astrophys. J. 635, 1087–1094 (2005).

    ADS  Google Scholar 

  122. 122.

    Ghez, A. M. et al. Measuring distance and properties of the milky way’s central supermassive black hole with stellar orbits. Astrophys. J. 689, 1044–1062 (2008).

    ADS  Google Scholar 

  123. 123.

    Gezari, S. et al. Adaptive optics near-infrared spectroscopy of the sagittarius A* cluster. Astrophys. J. 576, 790–797 (2002).

    ADS  Google Scholar 

  124. 124.

    Eisenhauer, F. et al. SINFONI in the galactic center: young stars and infrared flares in the central light-month. Astron. J. 628, 246–259 (2005).

    Google Scholar 

  125. 125.

    Collaboration, G. et al. First light for GRAVITY: phase referencing optical interferometry for the very large telescope interferometer. Astron. Astrophys. 602, A94 (2017).

    Google Scholar 

  126. 126.

    Collaboration, G. et al. Detection of the gravitational redshift in the orbit of the star S2 near the Galactic centre massive black hole. Astron. Astrophys. 615, L15 (2018).

    ADS  Google Scholar 

  127. 127.

    Mayor, M. & Queloz, D. A Jupiter-mass companion to a solar-type star. Nature 378, 355–359 (1995).

    ADS  Google Scholar 

  128. 128.

    Bowler, B. P. Imaging extrasolar giant planets. Publ. Astron. Soc. Pac. 128, 102001 (2016).

    ADS  Google Scholar 

  129. 129.

    Milli, J. et al. Near-infrared scattered light properties of the HR 4796 A dust ring. A measured scattering phase function from 13.6° to 166.6°. Astron. Astrophys. 599, A108 (2017).

    Google Scholar 

  130. 130.

    Macintosh, B. et al. First light of the gemini planet imager. Proc. Natl Acad. Sci. USA 111, 12661–12666 (2014).

    ADS  Google Scholar 

  131. 131.

    Chauvin, G. et al. A giant planet candidate near a young brown dwarf: direct VLT/NACO observations using IR wavefront sensing. Astron. Astrophys. 425, L29–L32 (2004).

    ADS  Google Scholar 

  132. 132.

    Marois, C. et al. Direct Imaging of multiple planets orbiting the star HR 8799. Science 322, 1348–1352 (2008).

    ADS  Google Scholar 

  133. 133.

    Marois, C., Zuckerman, B., Konopacky, Q. M., Macintosh, B. & Barman, T. Images of a fourth planet orbiting HR 8799. Nature 468, 1080–1083 (2010).

    ADS  Google Scholar 

  134. 134.

    Lagrange, A.-M. et al. A probable giant planet imaged in the β Pictoris disk: VLT/NaCo deep L’-band imaging. Astron. Astrophys. 493, L21–L25 (2008).

    ADS  Google Scholar 

  135. 135.

    Lagrange, A.-M. et al. A giant planet imaged in the disk of the young star beta Pictoris. Science 329, 57–59 (2010).

    ADS  Google Scholar 

  136. 136.

    Bonnefoy, M. et al. High angular resolution detection of β Pictoris b at 2.18 μm. Astron. Astrophys. 528, L15 (2011).

    ADS  Google Scholar 

  137. 137.

    Males, J. R. et al. Magellan adaptive optics first-light observations of the exoplanet β pic b. I. direct imaging in the far-red optical with MagAO + VisAO and in the near-ir with nici. Astrophys. J. 786, 32 (2014).

    ADS  Google Scholar 

  138. 138.

    Baudino, J.-L. et al. Interpreting the photometry and spectroscopy of directly imaged planets: a new atmospheric model applied to β Pictoris b and SPHERE observations. Astron. Astrophys. 582, A83 (2015).

    Google Scholar 

  139. 139.

    Morzinski, K. M. et al. Magellan Adaptive Optics first-light observations of the exoplanet beta Pic b. II. 3-5 micron direct imaging with MagAO + Clio, and the empirical bolometric luminosity of a self-luminous giant planet. Astrophys. J. 815, 108 (2015).

    ADS  Google Scholar 

  140. 140.

    Chilcote, J. et al. 1–2.4 μm Near-IR spectrum of the giant planet β pictoris b obtained with the gemini planet imager. Astrophys. J. 153, 182 (2017).

    Google Scholar 

  141. 141.

    Nielsen, E. L. et al. The gemini planet imager exoplanet survey: dynamical mass of the exoplanet β pictoris b from combined direct imaging and astrometry. Astrophys. J. 159, 71 (2020).

    Google Scholar 

  142. 142.

    Bowler, B. P., Liu, M. C., Dupuy, T. J. & Cushing, M. C. Near-infrared spectroscopy of the extrasolar planet HR 8799 b. Astrophys. J. 723, 850 (2010).

    ADS  Google Scholar 

  143. 143.

    Currie, T. et al. A combined Subaru/VLT/MMT 1–5 μm study of planets orbiting HR 8799: Implications for atmospheric properties, masses, and formation. Astrophys. J. 729, 128 (2011).

    ADS  Google Scholar 

  144. 144.

    Ingraham, P. et al. Gemini planet imager spectroscopy of the HR 8799 planets c and d. Astrophys. J. 794, L15 (2014).

    ADS  Google Scholar 

  145. 145.

    Skemer, A. J. et al. Directly imaged LT transition exoplanets in the mid-infrared. Astrophys. J. 792, 17 (2014).

    ADS  Google Scholar 

  146. 146.

    Barman, T. S., Konopacky, Q. M., Macintosh, B. & Marois, C. Simultaneous detection of water, methane, and carbon monoxide in the atmosphere of exoplanet hr 8799 b. Astrophys. J. 804, 61 (2015).

    ADS  Google Scholar 

  147. 147.

    Wang, J. J. et al. Dynamical constraints on the HR 8799 planets with GPI. Astrophys. J. 156, 192 (2018).

    Google Scholar 

  148. 148.

    Rameau, J. et al. Discovery of a probable 4-5 Jupiter-mass exoplanet to HD 95086 by direct-imaging. Astrophys. J. Lett. 772, L15 (2013).

    ADS  Google Scholar 

  149. 149.

    Bailey, V. et al. HD 106906 b: A planetary-mass companion outside a massive debris disk. Astrophys. J. 780, L4 (2013).

    ADS  Google Scholar 

  150. 150.

    Macintosh, B. et al. Discovery and spectroscopy of the young jovian planet 51 Eri b with the gemini planet imager. Science 350, 64–67 (2015).

    ADS  Google Scholar 

  151. 151.

    Keppler, M. et al. Discovery of a planetary-mass companion within the gap of the transition disk around PDS 70. Astron. Astrophys. 617, A44 (2018).

    Google Scholar 

  152. 152.

    Haffert, S. Y. et al. Two accreting protoplanets around the young star PDS 70. Nat. Astron. 3, 749–754 (2019).

    ADS  Google Scholar 

  153. 153.

    Stone, J. M. et al. The LEECH exoplanet imaging survey: limits on planet occurrence rates under conservative assumptions. Astrophys. J. 156, 286 (2018).

    Google Scholar 

  154. 154.

    Nielsen, E. L. et al. The gemini planet imager exoplanet survey: giant planet and brown dwarf demographics from 10 to 100 au. Astrophys. J. 158, 13 (2019).

    Google Scholar 

  155. 155.

    Chen, C. et al. Multiband GPI imaging of the HR 4796A debris disk. Astrophys. J. 898, 55 (2020).

    ADS  Google Scholar 

  156. 156.

    Jovanovic, N. et al. The subaru coronagraphic extreme adaptive optics system: enabling high-contrast imaging on solar-system scales. Publ. Astron. Soc. Pac. 127, 890–910 (2015).

    ADS  Google Scholar 

  157. 157.

    Males, J. R. et al. MagAO-X: project status and first laboratory results. in Adaptive Optics Systems VI Vol. 10703 1070309 (SPIE, 2018).

  158. 158.

    Roorda, A. & Williams, D. R. The arrangement of the three cone classes in the living human eye. Nature 397, 520–522 (1999).

    ADS  Google Scholar 

  159. 159.

    Laforest, T. et al. Transscleral optical phase imaging of the human retina. Nat. Photonics 14, 439–445 (2020).

    ADS  Google Scholar 

  160. 160.

    Tam, J., Tiruveedhula, P. & Roorda, A. Characterization of single-file flow through human retinal parafoveal capillaries using an adaptive optics scanning laser ophthalmoscope. Biomed. Opt. Express 2, 781–793 (2011).

    Google Scholar 

  161. 161.

    Mo, S. et al. Imaging foveal microvasculature: optical coherence tomography angiography versus adaptive optics scanning light ophthalmoscope fluorescein angiography. Invest. Ophth. Vis. Sci. 57, OCT130–OCT40 (2016).

    Google Scholar 

  162. 162.

    Cunefare, D. et al. RAC-CNN: multimodal deep learning based automatic detection and classification of rod and cone photoreceptors in adaptive optics scanning light ophthalmoscope images. Biomed. Opt. Express 10, 3815–3832 (2019).

    Google Scholar 

  163. 163.

    Ivers, K. M. et al. In vivo changes in lamina cribrosa microarchitecture and optic nerve head structure in early experimental glaucoma. PLoS ONE 10, e0134223 (2015).

    Google Scholar 

  164. 164.

    Burns, S. A. et al. In vivo adaptive optics microvascular imaging in diabetic patients without clinically severe diabetic retinopathy. Biomed. Opt. Express 5, 961–974 (2014).

    Google Scholar 

  165. 165.

    Zhang, F. et al. Revealing how color vision phenotype and genotype manifest in individual cone cells. Investig. Ophthalmol. Vis. Sci. 62, 8 (2021).

    Google Scholar 

  166. 166.

    Bedggood, P. & Metha, A. Mapping flow velocity in the human retinal capillary network with pixel intensity cross correlation. PLoS ONE 14, e0218918 (2019).

    Google Scholar 

  167. 167.

    Bek, T. Fine structure in diabetic retinopathy lesions as observed by adaptive optics imaging. A qualitative study. Acta Ophthalmol. 92, 753–758 (2014).

    Google Scholar 

  168. 168.

    Bedggood, P. & Metha, A. Direct visualization and characterization of erythrocyte flow in human retinal capillaries. Biomed. Opt. Express 3, 3264–3277 (2012).

    Google Scholar 

  169. 169.

    Rha, J. et al. Adaptive optics flood-illumination camera for high speed retinal imaging. Opt. Express 14, 4552–4569 (2006).

    ADS  Google Scholar 

  170. 170.

    Rossi, E. A. et al. Imaging individual neurons in the retinal ganglion cell layer of the living eye. Proc. Natl Acad. Sci. USA 114, 586–591 (2017).

    ADS  Google Scholar 

  171. 171.

    Guevara-Torres, A., Joseph, A. & Schallek, J. B. Label free measurement of retinal blood cell flux, velocity, hematocrit and capillary width in the living mouse eye. Biomed. Opt. Express 7, 4228–4249 (2016).

    Google Scholar 

  172. 172.

    Guevara-Torres, A., Williams, D. R. & Schallek, J. B. Imaging translucent cell bodies in the living mouse retina without contrast agents. Biomed. Opt. Express 6, 2106–2119 (2015).

    Google Scholar 

  173. 173.

    Scoles, D., Sulai, Y. N. & Dubra, A. In vivo dark-field imaging of the retinal pigment epithelium cell mosaic. Biomed. Opt. Express 4, 1710–23 (2013).

    Google Scholar 

  174. 174.

    Qin, Z. et al. Adaptive optics two-photon microscopy enables near-diffraction-limited and functional retinal imaging in vivo. Light Sci. Appl. 9, 79 (2020).

    ADS  Google Scholar 

  175. 175.

    Cua, M. et al. Coherence-gated sensorless adaptive optics multiphoton retinal imaging. Sci. Rep. 6, 32223 (2016).

    ADS  Google Scholar 

  176. 176.

    Sharma, R., Williams, D. R., Palczewska, G., Palczewski, K. & Hunter, J. J. Two-photon autofluorescence imaging reveals cellular structures throughout the retina of the living primate eye. Invest. Ophth. Vis. Sci. 57, 632–46 (2016).

    Google Scholar 

  177. 177.

    Yin, L. et al. Imaging light responses of retinal ganglion cells in the living mouse eye. J. Neurophysiol. 109, 2415–2421 (2013).

    Google Scholar 

  178. 178.

    Yin, L. et al. Imaging light responses of foveal ganglion cells in the living macaque eye. J. Neurosci. 34, 6596–6605 (2014).

    Google Scholar 

  179. 179.

    Zawadzki, R. J. et al. Adaptive-optics SLO imaging combined with widefield OCT and SLO enables precise 3D localization of fluorescent cells in the mouse retina. Biomed. Opt. Express 6, 2191–2210 (2015).

    Google Scholar 

  180. 180.

    Jung, H., Liu, T., Liu, J., Huryn, L. A. & Tam, J. Combining multimodal adaptive optics imaging and angiography improves visualization of human eyes with cellular-level resolution. Commun. Biol. 1, 189 (2018).

    Google Scholar 

  181. 181.

    Morgan, J. I. W., Dubra, A., Wolfe, R., Merigan, W. H. & Williams, D. R. In vivo autofluorescence imaging of the human and macaque retinal pigment epithelial cell mosaic. Invest. Ophth. Vis. Sci. 50, 1350 (2009).

    Google Scholar 

  182. 182.

    Rossi, E. A. et al. In vivo imaging of retinal pigment epithelium cells in age related macular degeneration. Biomed. Opt. Express 4, 2527–2539 (2013).

    Google Scholar 

  183. 183.

    Xu, X. et al. Retinal pigment epithelium degeneration associated with subretinal drusenoid deposits in age-related macular degeneration. Am. J. Ophthalmol. 175, 87–98 (2017).

    Google Scholar 

  184. 184.

    Takayama, K. et al. High-resolution imaging of the retinal nerve fiber layer in normal eyes using adaptive optics scanning laser ophthalmoscopy. PLoS ONE 7, e33158 (2012).

    ADS  Google Scholar 

  185. 185.

    Huang, G. et al. Imaging glaucomatous damage across the temporal raphe. Invest. Ophth. Vis. Sci. 56, 3496–504 (2015).

    Google Scholar 

  186. 186.

    Jonnal, R. S. et al. A Review of adaptive optics optical coherence tomography: technical advances, scientific applications, and the future. Invest. Ophth. Vis. Sci. 57, OCT51–OCT68 (2016).

    Google Scholar 

  187. 187.

    Pircher, M. & Zawadzki, R. J. Review of adaptive optics OCT (AO-OCT): principles and applications for retinal imaging [Invited]. Biomed. Opt. Express 8, 2536–2562 (2017).

    Google Scholar 

  188. 188.

    Zdankowski, P., McGloin, D. & Swedlow, J. R. Full volume super-resolution imaging of thick mitotic spindle using 3D AO STED microscope. Biomed. Opt. Express 10, 1999–2009 (2019).

    Google Scholar 

  189. 189.

    Patton, B. R. et al. Three-dimensional STED microscopy of aberrating tissue using dual adaptive optics. Opt. Express 24, 8862 (2016).

    ADS  Google Scholar 

  190. 190.

    Huang, F. et al. Ultra-high resolution 3D imaging of whole cells. Cell 166, 1028–1040 (2016).

    Google Scholar 

  191. 191.

    Turcotte, R., Liang, Y. & Ji, N. Adaptive optical versus spherical aberration corrections for in vivo brain imaging. Biomed. Opt. Express 8, 3891–3902 (2017).

    Google Scholar 

  192. 192.

    Sun, W., Tan, Z., Mensh, B. D. & Ji, N. Thalamus provides layer 4 of primary visual cortex with orientation- and direction-tuned inputs. Nat. Neurosci. 19, 308–315 (2015).

    Google Scholar 

  193. 193.

    Li, K. Y., Tiruveedhula, P. & Roorda, A. Intersubject variability of foveal cone photoreceptor density in relation to eye length. Invest. Ophth. Vis. Sci. 51, 6858–6867 (2010).

    Google Scholar 

  194. 194.

    Song, H., Chui, T. Y. P., Zhong, Z., Elsner, A. E. & Burns, S. A. Variation of cone photoreceptor packing density with retinal eccentricity and age. Invest. Ophth. Vis. Sci. 52, 7376–7384 (2011).

    Google Scholar 

  195. 195.

    Wang, Y. et al. Human foveal cone photoreceptor topography and its dependence on eye length. eLife 8, e47148 (2019).

    Google Scholar 

  196. 196.

    Curcio, C. A., Sloan, K. R., Kalina, R. E. & Hendrickson, A. E. Human photoreceptor topography. J. Comp. Neurol. 292, 497–523 (1990).

    Google Scholar 

  197. 197.

    Bedggood, P. A., Ashman, R., Smith, G. & Metha, A. B. Multiconjugate adaptive optics applied to an anatomically accurate human eye model. Opt. Express 14, 8019–8030 (2006).

    ADS  Google Scholar 

  198. 198.

    Laslandes, M., Salas, M., Hitzenberger, C. K. & Pircher, M. Increasing the field of view of adaptive optics scanning laser ophthalmoscopy. Biomed. Opt. Express 8, 4811–4826 (2017).

    Google Scholar 

  199. 199.

    Zawadzki, R. J. et al. Ultrahigh-resolution optical coherence tomography with monochromatic and chromatic aberration correction. Opt. Express 16, 8126–8143 (2008).

    ADS  Google Scholar 

  200. 200.

    Laser Institute of America. American National Standard for Safe Use of Lasers (2014).

  201. 201.

    Sredar, N., Fagbemi, O. E. & Dubra, A. Sub-airy confocal adaptive optics scanning ophthalmoscopy. Transl. Vis. Sci. Technol. 7, 17 (2018).

    Google Scholar 

  202. 202.

    Shroff, S. A., Fienup, J. R. & Williams, D. R. Phase-shift estimation in sinusoidally illuminated images for lateral superresolution. J. Opt. Soc. Am. A 26, 413–424 (2009).

    ADS  Google Scholar 

  203. 203.

    DuBose, T. B., LaRocca, F., Farsiu, S. & Izatt, J. A. Super-resolution retinal imaging using optically reassigned scanning laser ophthalmoscopy. Nat. Photonics 13, 257–262 (2019).

    ADS  Google Scholar 

  204. 204.

    Paudel, H. P., Taranto, J., Mertz, J. & Bifano, T. Axial range of conjugate adaptive optics in two-photon microscopy. Opt. Express 23, 20849–20857 (2015).

    ADS  Google Scholar 

  205. 205.

    Horstmeyer, R., Ruan, H. & Yang, C. Guidestar-assisted wavefront-shaping methods for focusing light into biological tissue. Nat. Photonics 9, 563–571 (2015).

    ADS  Google Scholar 

  206. 206.

    Mosk, A. P., Lagendijk, A., Lerosey, G. & Fink, M. Controlling waves in space and time for imaging and focusing in complex media. Nat. Photonics 6, 283–292 (2012).

    ADS  Google Scholar 

  207. 207.

    Yoon, S. et al. Deep optical imaging within complex scattering media. Nat. Rev. Phys. 2, 141–158 (2020).

    Google Scholar 

  208. 208.

    McCarthy, P. J. et al. Overview and status of the giant magellan telescope project. in Ground-based and Airborne Telescopes VII Vol. 10700 1070012 (SPIE, 2018).

  209. 209.

    Skidmore, W., Anupama, G. C. & Srianand, R. The Thirty Meter Telescope International Observatory facilitating transformative astrophysical science. Curr. Sci. 113, 639–648 (2017).

    ADS  Google Scholar 

  210. 210.

    Marchiori, G., Rampini, F., Ghedin, L. & Bressan, R. ELT design status: the most powerful ground telescope. in Ground-based and Airborne Telescopes VII Vol. 10700 1070021 (SPIE, 2018).

  211. 211.

    Vernet, E. et al. Adaptive optics at the ESO ELT. in Adaptive Optics Systems VI Vol. 10703 1070310 (SPIE, 2018).

  212. 212.

    Crane, J. et al. NFIRAOS adaptive optics for the thirty meter telescope. in Adaptive Optics Systems VI Vol. 10703 107033V (SPIE, 2018).

  213. 213.

    Bouchez, A. H. et al. An overview and status of GMT active and adaptive optics. in Adaptive Optics Systems VI Vol. 10703 107030W (SPIE, 2018).

  214. 214.

    Cunefare, D. et al. Deep learning based detection of cone photoreceptors with multimodal adaptive optics scanning light ophthalmoscope images of achromatopsia. Biomed. Opt. Express 9, 3740–3756 (2018).

    Google Scholar 

  215. 215.

    Kyono, T. et al. Machine learning for quality assessment of ground-based optical images of satellites. Opt. Eng. 59, 051403 (2020).

    ADS  Google Scholar 

  216. 216.

    Cumming, B. P. & Gu, M. Direct determination of aberration functions in microscopy by an artificial neural network. Opt. Express 28, 14511–14521 (2020).

    ADS  Google Scholar 

  217. 217.

    Saha, D. et al. Practical sensorless aberration estimation for 3D microscopy with deep learning. Opt. Express 28, 29044 (2020).

    ADS  Google Scholar 

  218. 218.

    Andersen, T., Owner-Petersen, M. & Enmark, A. Image-based wavefront sensing for astronomy using neural networks. J. Astron. Telesc. Instrum. Syst. 6, 1 (2020).

    Google Scholar 

  219. 219.

    Kam, Z., Hanser, B., Gustafsson, M. G. L., Agard, D. A. & Sedat, J. W. Computational adaptive optics for live three-dimensional biological imaging. Proc. Natl Acad. Sci. USA. 98, 3790–3795 (2001).

    ADS  Google Scholar 

  220. 220.

    Iyer, R. R., Liu, Y.-Z. & Boppart, S. A. Automated sensorless single-shot closed-loop adaptive optics microscopy with feedback from computational adaptive optics. Opt. Express 27, 12998–13014 (2019).

    ADS  Google Scholar 

  221. 221.

    Kner, P. Phase diversity for three-dimensional imaging. J. Opt. Soc. Am. 30, 1980 (2013).

    ADS  Google Scholar 

  222. 222.

    Tyson, R. K. Adaptive optics and ground-to-space laser communications. Appl. Opt. 35, 3640–3646 (1996).

    ADS  Google Scholar 

  223. 223.

    Chang, H. et al. Performance analysis of adaptive optics with a phase retrieval algorithm in orbital-angular-momentum-based oceanic turbulence links. Appl. Opt. 58, 6085–6090 (2019).

    ADS  Google Scholar 

  224. 224.

    Salter, P. S. & Booth, M. J. Adaptive optics in laser processing. Light Sci. Appl. 8, 110 (2019).

    ADS  Google Scholar 

  225. 225.

    Lubeigt, W., Grol, P. van, Valentine, G. & Burns, D. Use of intracavity adaptive optics in solid-state lasers operation at 1 µm. in Adaptive Optics for Industry and Medicine 217–227 (Springer, 2005).

Download references


D.T.M. and K.K. acknowledge support from the NIH grants R01 EY018339 and R01 EY029808. N.J. acknowledges support from the NIH grant U01NS103489. M.J.B., K.M.H. and R.T. acknowledge support from the European Research Council 695140.

Author information




Introduction (M.J.B., K.M.H. and R.T.); Experimentation (M.J.B., K.M.H. and R.T.); Results (M.J.B., D.T.M., K.K., J.R.M. and N.J.); Applications (M.J.B., D.T.M., K.K., J.R.M. and N.J.); Reproducibility and data deposition (M.J.B., R.T., D.T.M., K.K., J.R.M. and N.J.); Limitations and optimizations (M.J.B., D.T.M., K.K., J.R.M. and N.J.); Outlook (M.J.B., K.M.H. and R.T.); Overview of the Primer (M.J.B.).

Corresponding author

Correspondence to Martin J. Booth.

Ethics declarations

Competing interests

D.T.M. and K.K. have a patent on AO-OCT technology. Both authors stand to benefit financially from any commercialization of the technology. N.J. has two patents on AO microscopy technology. M.J.B. holds patents on adaptive optics technology and has significant interests in the companies Opsydia Ltd and Aurox Ltd. Otherwise, the authors are not aware of any affiliations, memberships, funding or financial holdings that might be perceived as affecting the objectivity of this publication. K.M.H., R.T. and J.R.M. declare no competing interests.

Additional information

Peer review information

Nature Reviews Methods Primers thanks V. Chambouleyron, B. Neichel and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Related links



Keck NIRC2 imager:


The Gemini Science Archive:

The Keck Observatory Archive:

The Subaru Telescope Archive System:

The ESO Science Archive Facility:

Supplementary information


Optical field

Describes the distribution of light as an electrical field across space and time in terms of amplitude, phase, frequency and polarization.


Reduction of an effect by modulation of the optical field through introducing the opposite effect.


All rays being brought to meet at one point.

Optical path length

The length of the path followed by a light ray multiplied by the refractive index of the medium.

Pupil plane

Aperture stop location.


All rays are parallel to each other.


There are no aberrations present in the focus. The minimum focal diameter is limited by diffraction owing to the wave nature of light.

Focal length

The distance between a lens and where the rays meet the optical axis for incoming collimated light.

Strehl ratio

The ratio of the intensity of the peak of the aberrated point spread function (PSF) to that of the diffraction-limited PSF.

Noll convention

Mathematical description of aberrated wavefront shapes as proposed by Noll.


Miniature lenses usually as part of an array.


Elements that deform the mirror.

Dynamic range

The range between the smallest and largest measurable values.


A mathematical function basis that is confined in both space and frequency.

Influence function

The shape of modulation produced by a device when a signal, such as voltage, is sent to one actuator or pixel.

Monochromatic polarized light

Light of a single wavelength with a structured oscillation of the electric field.


Maximal physical distance that an adaptive element can move, which limits the optical path length of phase modulation that can be imparted.

Phase wrapping

Representation of the phase information within the range [0,2π] or [−π, π] radians by adding or subtracting multiples of 2π.

Closed-loop bandwidth

The maximum frequency fluctuation that an adaptive optics system can fully or partially correct.

Flood illumination

A traditional ophthalmoscopy modality based on flash photography in which the image of the illuminated retina is captured by an area detector.


100 nm-sized vesicles that are used to bring substances inside the cell.


Specialized subunit within a cell with a specific function such as the Golgi complex, the endoplasmic reticulum or the mitochondrion.

Growth cone

Subcellular machinery used for cell migration.


Junctions between neurons through which information flows.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Hampson, K.M., Turcotte, R., Miller, D.T. et al. Adaptive optics for high-resolution imaging. Nat Rev Methods Primers 1, 68 (2021).

Download citation


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing