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

CalibFPA: A Focal Plane Array Imaging System based on Online Deep-Learning Calibration