  Compressive focal plane arrays (FPA) enable cost-effective high-resolution
(HR) imaging by acquisition of several multiplexed measurements on a
low-resolution (LR) sensor. Multiplexed encoding of the visual scene is
typically performed via electronically controllable spatial light modulators
(SLM). An HR image is then reconstructed from the encoded measurements by
solving an inverse problem that involves the forward model of the imaging
system. To capture system non-idealities such as optical aberrations, a
mainstream approach is to conduct an offline calibration scan to measure the
system response for a point source at each spatial location on the imaging
grid. However, it is challenging to run calibration scans when using structured
SLMs as they cannot encode individual grid locations. In this study, we propose
a novel compressive FPA system based on online deep-learning calibration of
multiplexed LR measurements (CalibFPA). We introduce a piezo-stage that
locomotes a pre-printed fixed coded aperture. A deep neural network is then
leveraged to correct for the influences of system non-idealities in multiplexed
measurements without the need for offline calibration scans. Finally, a deep
plug-and-play algorithm is used to reconstruct images from corrected
measurements. On simulated and experimental datasets, we demonstrate that
CalibFPA outperforms state-of-the-art compressive FPA methods. We also report
analyses to validate the design elements in CalibFPA and assess computational
complexity.
