Imaging Through Deconvolution with a Spatially-Variant Point Spread Function

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.