Kao Kitichotkul

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 reconstruction by 3 to 4 dB compared to conventional methods under optimal incident flux conditions. Additionally, this method reduces the required number of readout frames to achieve the same mean-squared error as other methods by a factor of 4. Finally, our method matches the Cramer-Rao bound for detection probability estimation for a wider range of incident flux values compared to conventional methods. While demonstrated for a specific detector type and readout architecture, this method can be extended to more general multiplexing with different detector models.

Single-photon lidar (SPL) can accurately measure distances to targets from extremely weak reflections. However, the conventional wisdom holds that pulsed lidar cannot directly measure velocity, unlike other forms of lidar. We present a detection model for SPL that explicitly includes a target’s radial velocity, manifesting as a Doppler shift in the repetition frequency of the received laser pulse train. We propose an approach called Doppler SPL, comprising a pair of methods for jointly estimating range and velocity. Our first method estimates the Doppler shift via Fourier analysis of the detection times. The second method is a maximum likelihood (ML) estimator that can improve the Fourier estimate and enable joint estimation of the flux from signal and background sources. We derive the Cramér–Rao bound for the estimation problem and show via simulations that the ML estimator is statistically efficient across diverse acquisition settings. We also demonstrate simultaneous estimation of range with sub-centimeter accuracy and velocity with 0.1 m/s root mean square error for experimental measurements at 50 frames per second (fps), despite a signal-to-background ratio as low as 0.007. Finally, we present an example of a 3D video reconstruction at 120 fps with per-pixel velocity estimates. With the addition of velocimetry, Doppler SPL has the potential to introduce advanced capabilities in applications such as atmospheric monitoring or autonomous navigation.

Single-photon lidar (SPL) can achieve high-accuracy, lowlight ranging; however, velocity estimation typically requires regression over multiple distance measurements. Here, we introduce Doppler SPL, which enables joint instantaneous velocity and range estimation. First, we derive a measurement model for SPL, showing that a target moving at a constant velocity introduces a Doppler shift into the sequence of photon detection times. We then introduce estimators for range and velocity based on Fourier analysis of the detection time sequence. Simulations show improved accuracy of our method over baseline approaches, and we further validate our approach on experimental SPL data for a moving target.

The noise in secondary electron (SE) imaging comes from variation in the number of detected SEs and detector imperfections. We recognize that the SE variation itself has two contributors—the source and the sample—that can be separated by closer examination of the signal produced by the detector. Treating SE imaging as a quantitative modality with the goal of pixelwise estimation of SE yield, we use statistically principled techniques to substantially mitigate source noise. We demonstrate a factor of 3 reduction in the required imaging dose. This work opens up broad avenues for quantitative sample mapping at increased speeds and reduced beam currents and characterization of fragile biological samples with electron and ion beams at low- to medium-keV energies. Modern science is dependent on imaging on the nanoscale, often achieved through processes that detect secondary electrons created by a highly focused incident charged particle beam. Multiple types of measurement noise limit the ultimate trade-off between the image quality and the incident particle dose, which can preclude useful imaging of dose-sensitive samples. Existing methods to improve image quality do not fundamentally mitigate the noise sources. Furthermore, barriers to assigning a physically meaningful scale make the images qualitative. Here, we introduce ion count-aided microscopy (ICAM), which is a quantitative imaging technique that uses statistically principled estimation of the secondary electron yield. With a readily implemented change in data collection, ICAM substantially reduces source shot noise. In helium ion microscopy, we demonstrate 3x dose reduction and a good match between these empirical results and theoretical performance predictions. ICAM facilitates imaging of fragile samples and may make imaging with heavier particles more attractive.

In direct time-of-flight single-photon lidar, the photon detection times are typically used to estimate the depth, while the number of detections is used to estimate the reflectivity. This paper examines the use of detection times in reflectivity estimation with a free-running SPAD by proposing new estimators and unifying previous results with new analysis. In the low-flux regime where dead times are negligible, we examine the Cramér–Rao bound of reflectivity estimation. When depth is unknown, we show that an estimator based on censoring can perform almost as well as a maximum likelihood estimator, and, surprisingly, incorrect depth estimation can reduce the mean-squared errors of reflectivity estimation. We also examined joint estimation of signal and background fluxes, for which our proposed censoring-based estimator performs as well as the maximum likelihood estimator. In the high-flux regime where dead times are not negligible, we model the detection times as a Markov chain and examine some reflectivity estimators that exploit the detection times.

In a particle beam microscope, a raster-scanned focused beam of particles interacts with a sample to generate a secondary electron (SE) signal pixel by pixel. Conventionally formed micrographs are noisy because of limitations on acquisition time and dose. Recent work has shown that estimation methods applicable to a time-resolved measurement paradigm can greatly reduce noise, but these methods apply pixel by pixel without exploiting image structure. Raw SE count data can be modeled with a compound Poisson (Neyman Type A) likelihood, which implies data variance that is signal-dependent and greater than the variation in the underlying particle-sample interaction. These statistical properties make methods that assume additive white Gaussian noise ineffective. This article introduces methods for particle beam micrograph denoising that use the plug-and-play framework to exploit image structure while being applicable to the unusual data likelihoods of this modality. Approximations of the data likelihood that vary in accuracy and computational complexity are combined with denoising by total variation regularization, BM3D, and DnCNN. Methods are provided for both conventional and time-resolved measurements, assuming SE counts are available. In simulations representative of helium ion microscopy and scanning electron microscopy, significant improvements in root mean-squared error (RMSE), structural similarity index measure (SSIM), and qualitative appearance are obtained. Average reductions in RMSE are by factors ranging from 2.24 to 4.11.

Convolutional neural networks (CNN) have emerged as a powerful tool for solving computational imaging reconstruction problems. However, CNNs are generally difficult-to-understand black-boxes. Accordingly, it is challenging to know when they will work and, more importantly, when they will fail. This limitation is a major barrier to their use in safety-critical applications like medical imaging: Is that blob in the reconstruction an artifact or a tumor?In this work we use Stein’s unbiased risk estimate (SURE) to develop per-pixel confidence intervals, in the form of heatmaps, for compressive sensing reconstruction using the approximate message passing (AMP) framework with CNN-based denoisers. These heatmaps tell end-users how much to trust an image formed by a CNN, which could greatly improve the utility of CNNs in various computational imaging applications.