We make images sharper
Click here to see a recent story on our work on the James Webb Space Telescope.
Click here to see a recent story on Manuel Guizar’s winning the ICO Prize for 2019.
We perform research in the areas of:
- Unconventional imaging
- Phase retrieval
- Wavefront sensing
- Imaging with sparse-aperture telescopes
- Image reconstruction algorithms
Recently published papers
By Aaron Michalko: “Verification of transverse translation diverse phase retrieval for concave optical metrology,”Opt. Letters 43, 4827-4830 (2018).
“Transverse Translation Diverse Phase Retrieval Using Soft-Edged Illumination,” Opt. Lett. 43, 1331-1334 (2018).
By Wes Farriss: “Phase retrieval in generalized optical interferometry systems,” Opt. Express 26, 2191-2202 (2018). T. Malhotra, W.E. Farriss, J. Hassett, A.F. Abouraddy, J.R. Fienup, and A.N. Vamivakas,
“Interferometric spatial mode analyzer with a bucket detector,” Opt. Express 26, 8719-8728 (2018).
By Scott Paine: “Machine learning for improved image-based wavefront sensing,” Opt. Lett. 43, 1235-1238 (2018).
“Extending capture range for piston retrieval in segmented systems,” Appl. Opt. 56, 9186-9192 (2017).
By Alex Iacchetta: “Wide-Field Spatiospectral Interferometry: Theory and Imaging Properties,” J. Opt. Soc. Am. A 34, 1896-1907 (2017). It was highlighted as the “Editors Pick.”
By Alden Jurling and Matt Bergkoetter: “Techniques for arbitrary sampling in two-dimensional Fourier transforms,” J. Opt. Soc. Am. A35, 1784-1796 (2018).
By Jim Fienup: “Direct-detection Synthetic-aperture Coherent Imaging by Phase Retrieval,” Opt. Eng. 56, (2017) dx.doi.org/10.1117/1.OE.56.11.113111 (13 pp).
A famous paper
One of Prof. Fienup’s papers [J.R. Fienup, “Phase Retrieval Algorithms: a Comparison,” Appl. Opt. 21, 2758-2769 (1982)] has received over 4,800 citations (Google Scholar) and is the most highly cited paper (out of over 50,000) in the journal Applied Optics.
Examples of research problems:
NASA’s James Webb Space Telescope will need phase retrieval to align the 18 segments of the primary mirror. Similar phase retrieval algorithms were used to determine how to fix the Hubble Space Telescope. NASA’s Wide-Field Infrared Survey Telescope (WFIRST), although not segmented, will need precise knowledge of its small wavefront aberrations for exoplanet direct imaging and microlensing, which can be determined with phase retrieval. NASA’s proposed Large UV/Optical/Infrared (LUVOIR) Surveyer telescope will include coronagraph instruments that will be able to image dim planets orbiting bright stars. It will require exquisite wavefront control and highly accurate modeling of the coronagraph optics.
Astronomers and DoD are interested in imaging interferometry
using two or more well-separated, small telescopes to obtain images having the fine resolution of a single, much larger telescope, as illustrated here by NASA’s SPace InfraRed Interferometric Telescope (SPIRIT) concept. We are interested in image and spectral reconstruction despite uncertainties in alignment and motion, incomplete data, and, for ground-based systems, atmospheric turbulence. We are currently applying these techniques to the problem of imaging geosynchronous satellites from the earth, despite atmospheric turbulence, and of wide-field infrared astronomical imaging.
Phase retrieval algorithms can be used to perform optical metrology, testing aspheric optical surfaces during their manufacture with a simple system not requiring a null lens. We are developing these ideas for measuring free-form optical surfaces.
Image sharpening algorithms can be used to estimate multiple phase screens throughout a volume of turbulence and reconstruct fine-resolution images of objects, despite the space-variant blurring effects of atmospheric turbulence.
Phase retrieval algorithms can be used to reconstruct fine-resolution images of satellites and astronomical objects, despite the blurring effects of atmospheric turbulence.
Telescopes having sparse apertures or are made up of an array of multiple smaller telescopes can give fine resolution images, while having large savings on size and weight. Image restoration and phase retrieval (to align the sub-apertures) are needed to achieve good quality imagery.