Publications

2026

1. ICASSP

   ProxiCBO: A consensus-based method for composite optimization

   Haoyu Zhang, Yanting Ma, Ruangrawee Kitichotkul, and 2 more authors

   In ICASSP 2026-2026 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2026

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   This paper presents an interacting-particle optimization method for composite optimization problems. The proposed approach combines ideas from consensus-based optimization (CBO) with proximal gradient descent. We establish theoretical convergence guarantees for the continuous-time finite-particle dynamics and introduce an alternating update scheme for practical implementation. Finally, we validate the effectiveness of our method across several signal processing problems, demonstrating its advantages over proximal gradient descent, CBO, and their variants.

2. ICASSP

   Equivariant Deep Equilibrium Models for Imaging Inverse Problems

   Alexander Mehta, Ruangrawee Kitichotkul, Vivek K Goyal, and 1 more author

   In ICASSP 2026-2026 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2026

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   Equivariant imaging (EI) enables training signal reconstruction models without requiring ground truth data by leveraging signal symmetries. Deep equilibrium models (DEQs) are a powerful class of neural networks where the output is a fixed point of a learned operator. However, training DEQs with complex EI losses requires implicit differentiation through fixed-point computations, whose implementation can be challenging. We show that backpropagation can be implemented modularly, simplifying training. Experiments demonstrate that DEQs trained with implicit differentiation outperform those trained with Jacobian-free backpropagation and other baseline methods. Additionally, we find evidence that EI-trained DEQs approximate the proximal map of an invariant prior.

3. Optics Express

   Unambiguous range extension for Doppler single-photon lidar

   Ruangrawee Kitichotkul, Joshua Rapp, Yanting Ma, and 1 more author

   Optics Express, 2026

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   Single-photon lidar (SPL) measures the time delay between a laser pulse emission and a single photon detection. SPL typically uses a periodic sequence of illumination pulses to improve the accuracy of distance estimates, and the radial velocity of a moving target can also be determined from the Doppler shift in the pulse repetition frequency. Although increasing the repetition frequency increases the photon detection rate, it also decreases the unambiguous range. Several alternative pulsing modes have been proposed to break this tradeoff for SPL measurements of static scenes, e.g., using multiple repetition rates or random pulse trains to yield both high count rates and a long unambiguous range. In this work, we show that velocity can still be estimated despite the use of non-periodic range extension pulse patterns. We derive a general model for the photon acquisition process with a moving target and propose maximum likelihood estimators (MLEs) to recover the distance, velocity, and photon flux levels. Each range extension mode requires a tailored initialization scheme in order for our MLE solver to converge to the global optimum. We demonstrate through simulations and experiments that non-periodic SPL can simultaneously achieve long-range, high-accuracy distance and velocity estimates.

4. Thesis

   Single-photon lidar: statistical analysis, high-flux imaging, and velocimetry

   Ruangrawee Kitichotkul

   Boston University, 2026

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   Single-photon lidar (SPL) systems leverage the sensitivity of single-photon avalanche diodes (SPADs) to achieve long-range, high-resolution 3D imaging. Despite these strengths, the technology faces many critical challenges that limit its real-world deployment. This thesis addresses three limitations—the reliance on knowing the ambient background flux, performance degradation in high-flux environments due to SPAD dead time, and the inability to measure velocity—by developing probabilistic models and corresponding estimators. First, we introduce a joint maximum likelihood (ML) estimator that simultaneously determines signal flux, background flux, and depth, enabling real-time adaptation to varying ambient light without requiring prior flux knowledge. Second, we develop computationally efficient joint ML estimators for high-flux operation under SPAD dead time. Simulations and experiments show the clear superiority of the free-running mode, in which the SPAD is reactivated immediately after the dead time, over the conventional synchronous mode, in which reactivation occurs at the beginning of the next repetition period. The free-running mode is robustly depth-independent, outperforms the synchronous mode even under extreme dead-time conditions, and has a higher optimal flux than the conventional “5% rule.” For instance, at a signal-to-background ratio of 0.1, the optimal flux is approximately eight photons per repetition period, \ie, \sim800%. Finally, we pioneer Doppler SPL, a methodology that integrates target velocity into the probabilistic model as a Doppler shift in the pulsing repetition period. By employing Fourier analysis of Poisson processes and ML estimation, this approach jointly estimates velocity alongside all other scene parameters from absolute detection times. Collectively, these contributions significantly expand the operational envelope of SPL, transforming it into a robust, high-performance sensing modality capable of handling dynamic backgrounds, mitigating dead-time effects, and simultaneously measuring distance, velocity, and reflectivity.

2025

1. ICCV

   Free-running vs Synchronous: Single-Photon Lidar for High-flux 3D Imaging

   Ruangrawee Kitichotkul, Shashwath Bharadwaj, Joshua Rapp, and 3 more authors

   In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), Oct 2025

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   Conventional wisdom suggests that single-photon lidar (SPL) should operate in low-light conditions to minimize dead-time effects. Many methods have been developed to mitigate these effects in synchronous SPL systems. However, solutions for free-running SPL remain limited despite the advantage of reduced histogram distortion from dead times. To improve the accuracy of free-running SPL, we propose a computationally efficient joint maximum likelihood estimator of the signal flux, the background flux, and the depth using only histograms, along with a complementary regularization framework that incorporates a learned point cloud score model as a prior. Simulations and experiments demonstrate that free-running SPL yields lower estimation errors than its synchronous counterpart under identical conditions, with our regularization further improving accuracy.

2. CVPR

   Image Reconstruction from Readout-Multiplexed Single-Photon Detector Arrays

   Shashwath Bharadwaj, Ruangrawee Kitichotkul, Akshay Agarwal, and 1 more author

   In Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR), Jun 2025

   Highlight (13.5%)

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   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.

3. Optica

   Simultaneous range and velocity measurement with Doppler single-photon lidar

   Ruangrawee Kitichotkul, Joshua Rapp, Yanting Ma, and 1 more author

   Optica, May 2025

   12% Acceptance rate

   Abs DOI

   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.

4. ICASSP

   Doppler Single-Photon Lidar

   Ruangrawee Kitichotkul, Joshua Rapp, Yanting Ma, and 1 more author

   In ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), May 2025

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   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.

2024

1. PNAS

   Shot noise-mitigated secondary electron imaging with ion count-aided microscopy

   Akshay Agarwal, Leila Kasaei, Xinglin He, and 6 more authors

   Proceedings of the National Academy of Sciences, May 2024

   14% Acceptance rate

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   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.

2. CLEO

   Mitigating Misattributions in Single-Photon Detector Arrays with Row–Column Readouts

   Shashwath Bharadwaj, Ruangrawee Kitichotkul, Akshay Agarwal, and 1 more author

   In CLEO 2024, May 2024

   Abs

   A novel estimator resolves the ambiguity of spatial locations of multiphoton coincidences in single-photon detector arrays. This method mitigates misattributions introduced by multiplexed readout mechanisms that are commonly used to implement large-scale arrays.

3. IEEE JSTQE

   The Role of Detection Times in Reflectivity Estimation With Single-Photon Lidar

   Ruangrawee Kitichotkul, Joshua Rapp, and Vivek K Goyal

   IEEE Journal of Selected Topics in Quantum Electronics, May 2024

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   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.

2023

1. IEEE TCI

   Denoising Particle Beam Micrographs With Plug-and-Play Methods

   Minxu Peng, Ruangrawee Kitichotkul, Sheila W. Seidel, and 2 more authors

   IEEE Transactions on Computational Imaging, May 2023

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   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.

2021

1. ICASSP

   Suremap: Predicting Uncertainty in Cnn-Based Image Reconstructions Using Stein’s Unbiased Risk Estimate

   Ruangrawee Kitichotkul, Christopher A. Metzler, Frank Ong, and 1 more author

   In ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), May 2021

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   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.