Imaging through deconvolution with a spatially variant point spread function

Kyle Novak; Abbie T. Watnik

doi:10.1117/12.2585632

20 April 2021 Imaging through deconvolution with a spatially variant point spread function

Kyle Novak, Abbie T. Watnik

Author Affiliations +

Proceedings Volume 11731, Computational Imaging VI; 1173105 (2021) https://doi.org/10.1117/12.2585632
Event: SPIE Defense + Commercial Sensing, 2021, Online Only

Abstract

We demonstrate a technique for restoring imagery using a computational imaging camera with a phase mask that produces a blurred, space-variant point spread function (PSF). To recover arbitrary images, we first calibrate the computational imaging process utilizing Karheunen-Loeve Decomposition, where the PSFs are sampled across the field of view of the camera system. These PSFs can be transformed into a series of spatially invariant "eigen-PSFs", each with an associated coefficient matrix. Thus the act of performing a spatially varying image deconvolution can be changed into a weighted sum of spatially invariant deconvolutions. After demonstrating this process on simulated data, we also show real-world results from a camera system modified with a diffractive waveplate, and provide a brief discussion on processing time and tradeoffs inherent to the technique.

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Citation Download Citation

Kyle Novak and Abbie T. Watnik "Imaging through deconvolution with a spatially variant point spread function", Proc. SPIE 11731, Computational Imaging VI, 1173105 (20 April 2021); https://doi.org/10.1117/12.2585632

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