5 citations · 9 across the 6 of their papers we have counts for
7 papers
ProxiCBO: A Provably Convergent Consensus-Based Method for Composite Optimization
Haoyu Zhang, Yanting Ma, Ruangrawee Kitichotkul +2
This paper introduces an interacting-particle optimization method tailored to possibly non-convex composite optimization problems, which arise widely in signal processing. The prop…
Equivariant Deep Equilibrium Models for Imaging Inverse Problems
Alexander Mehta, Ruangrawee Kitichotkul, Vivek K Goyal +1
Equivariant imaging (EI) enables training signal reconstruction models without requiring ground truth data by leveraging signal symmetries. Deep equilibrium models (DEQs) are a pow…
Free-running vs. Synchronous: Single-Photon Lidar for High-flux 3D Imaging
Ruangrawee Kitichotkul, Shashwath Bharadwaj, Joshua Rapp +3
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…
Image Reconstruction from Readout-Multiplexed Single-Photon Detector Arrays
Shashwath Bharadwaj, Ruangrawee Kitichotkul, Akshay Agarwal +1
Readout multiplexing is a promising solution to overcome hardware limitations and data bottlenecks in imaging with single-photon detectors. Conventional multiplexed readout process…
Shot noise-mitigated secondary electron imaging with ion count-aided microscopy
Akshay Agarwal, Leila Kasaei, Xinglin He +6
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…
Denoising Particle Beam Micrographs with Plug-and-Play Methods
Minxu Peng, Ruangrawee Kitichotkul, Sheila W. Seidel +2
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…
SUREMap: Predicting Uncertainty in CNN-based Image Reconstruction Using Stein's Unbiased Risk Estimate
Ruangrawee Kitichotkul, Christopher A. Metzler, Frank Ong +1
Convolutional neural networks (CNN) have emerged as a powerful tool for solving computational imaging reconstruction problems. However, CNNs are generally difficult-to-understand b…