Imaging through turbulence using compressive coherence sensing.
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Previous studies have shown that the isoplanatic distortion due to turbulence and the image of a remote object may be jointly estimated from the 4D mutual intensity across an aperture. This Letter shows that decompressive inference on a 2D slice of the 4D mutual intensity, as measured by a rotational shear interferometer, is sufficient for estimation of sparse objects imaged through turbulence. The 2D slice is processed using an iterative algorithm that alternates between estimating the sparse objects and estimating the turbulence-induced phase screen. This approach may enable new systems that infer object properties through turbulence without exhaustive sampling of coherence functions.
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Michael J. Fitzpatrick Professor of Photonics in the Edmund T. Pratt, Jr. School of Engineering
David Brady leads the Duke Information Spaces Project (DISP). Historically, DISP has focused on computational imaging systems, with particular emphasis on smart cameras for security, consumer, transportation and broadcast applications. Currently DISP focuses primarily on the use of artificial intelligence in camera arrays for interactive broadcasting.
Associate Research Professor in the Department of Electrical and Computer Engineering
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