Image Reconstruction from Readout-Multiplexed Single-Photon Detector Arrays
Shashwath Bharadwaj, Ruangrawee Kitichotkul, Akshay Agarwal, Vivek K Goyal

TL;DR
This paper introduces a probabilistic inverse imaging framework for resolving multiple photon events in readout-multiplexed single-photon detector arrays, significantly improving image reconstruction quality and efficiency.
Contribution
It develops a novel estimator that resolves up to four coincident photons and demonstrates improved PSNR, reduced frame requirements, and theoretical optimality over conventional methods.
Findings
Increases PSNR by 3-4 dB compared to conventional methods.
Reduces required readout frames by a factor of ~4.
Matches the Cramer-Rao bound for detection probability estimation.
Abstract
Readout multiplexing is a promising solution to overcome hardware limitations and data bottlenecks in imaging with single-photon detectors. Conventional multiplexed readout processing creates an upper bound on photon counts at a very fine time scale, where frames with multiple detected photons must either be discarded or allowed to introduce significant bias. We formulate multiphoton coincidence resolution as an inverse imaging problem and introduce a solution framework to probabilistically resolve the spatial locations of photon incidences. Specifically, we develop a theoretical abstraction of row--column multiplexing and a model of photon events that make readouts ambiguous. Using this, we propose a novel estimator that spatially resolves up to four coincident photons. Monte Carlo simulations show that our proposed method increases the peak signal-to-noise ratio (PSNR) of…
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Taxonomy
TopicsPhotoacoustic and Ultrasonic Imaging · Advanced Fluorescence Microscopy Techniques · Advanced Optical Sensing Technologies
