University of Rochester
Institute of OpticsPRAISE
Professor Fienup
Group members

Phase Retrieval and Imaging Science Group
Prof. James R. Fienup

The Phase Retrieval and Imaging Science Group in the Intitute of Optics at the University of Rochester performs research in the areas of unconventional imaging, phase retrieval, wave-front sensing, imaging with sparse-aperture telescopes, and image reconstruction algorithms.

What we do (paper titles analyzed by www.wordle.net)
(Click on the picture and enlarge the resulting window to get a magnified version of it:)

News Flashes:

Zack DeSantis was named a finalist in the Emil Wolf Outstanding Student Paper Competition for his paper “Adaptive Allocation of Measurement Time for Imaging Interferometry” at the Frontiers in Optics (FiO) conference in October 2015 in San Jose, CA.

Scott Paine won third place in the Synopsis 2015 Robert S. Hilbert Memorial Optical Design Competition. His project, "Wide Angle Climbing Camera" earned him a $1,000.00 prize. He is the fifth student in our group to win this prize, along with Matt Bolcar, Manuel Guizar-Sicairos, Dustin Moore, and Matt Bergkoetter.

Alex Iaccetta won a NASA Space Technology Research Fellowship (NSTRF14), for his proposal titled "Astro-Interferometric Modeling and Spatio-Spectral Reconstruction."

Recently Published Papers
(PDFs of these papers can be downloaded from our web site after clicking on the Publications box to the left.)

By Dustin Moore: D.B. Moore and J.R. Fienup, “Ptychography for optical metrology with limited translation knowledge,” Appl. Opt. 55, 4596-4610 (2016).

“Subaperture translation estimation accuracy in transverse-translation diversity phase retrieval,” Appl. Opt. 55, 2526-2536 (2016).

By Alden Jurling: “Phase retrieval with unknown sampling factors via the two-dimensional chirp z-transform,” J. Opt. Soc. Am. A 31, 1904-1911 (2014).

“Applications of Algorithmic Differentiation to Phase Retrieval Algorithms,” J. Opt. Soc. Am. A 31, 1348-1359 (2014).

“Extended Capture Range for Focus Diverse Phase Retrieval in Segmented Aperture Systems Using Geometrical Optics,” J.Opt. Soc. Am. A 31, 661-666 (2014).

By Prof. Fienup: “Phase Retrieval Algorithms: a Personal Tour [Invited],” Appl. Opt. 52, 45-56 (2013).

An Ancient Paper

According to Thomson Reuter’s Web of Science, one of Prof. Fienup's papers [J.R. Fienup, “Phase Retrieval Algorithms: a Comparison,” Appl. Opt. 21, 2758-2769 (1982)] has received over 2,000 citations and has recently become the most highly cited paper (out of over 46,000) 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 exquisit knowledge of it small wavefront aberrations for exoplanet direct imaging and microlensing, which can be determined with phase retrieval.

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 astronmical 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.