TiDy-PSFs: Computational Imaging with Time-Averaged Dynamic Point-Spread-Functions

Sachin Shah, Sakshum Kulshrestha, Christopher A. Metzler; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2023, pp. 10657-10667


Point-spread-function (PSF) engineering is a powerful computational imaging technique wherein a custom phase mask is integrated into an optical system to encode additional information into captured images. Used in combination with deep learning, such systems now offer state-of-the-art performance at monocular depth estimation, extended depth-of-field imaging, lensless imaging, and other tasks. Inspired by recent advances in spatial light modulator (SLM) technology, this paper answers a natural question: Can one encode additional information and achieve superior performance by changing a phase mask dynamically over time? We first prove that the set of PSFs described by static phase masks is non-convex and that, as a result, time-averaged PSFs generated by dynamic phase masks are fundamentally more expressive. We then demonstrate, in simulation, that time-averaged dynamic (TiDy) phase masks can leverage this increased expressiveness to offer substantially improved monocular depth estimation and extended depth-of-field imaging performance.

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@InProceedings{Shah_2023_ICCV, author = {Shah, Sachin and Kulshrestha, Sakshum and Metzler, Christopher A.}, title = {TiDy-PSFs: Computational Imaging with Time-Averaged Dynamic Point-Spread-Functions}, booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)}, month = {October}, year = {2023}, pages = {10657-10667} }