arXiv subjects

Peter Menart

Publications and source records attributed to Peter Menart.

3 recordsLinked to original sources

arXiv2025

Poisson wavefront imaging in photon-starved scenarios

Low-photon phase imaging is essential in applications where the signal is limited by short exposure times, faint targets, or the need to protect delicate samples. We address this challenge with Poisson Wavefront Imaging (PWI), an optimization-based method that incorporates Poisson photon statistics and a smoothness prior to improve wavefront reconstruction. By using multiple spatial light modulator's phase patterns, PWI enhances Fisher information, boosting theoretical accuracy and regularizing the retrieval process effectively. In simulations, PWI approaches the theoretical phase error limit, and in experiments it reduces phase error by up to 1.6x compared to the Gerchberg-Saxton algorithm, achieving 1.8x higher resolution wavefront imaging in low photon regime. This method advances photon-limited imaging with applications in astronomy, semiconductor metrology, and biological systems.

physics.optics↗

arXiv2025

Photon-starved imaging through turbulence at the diffraction limit

Ground-based imaging systems struggle to achieve diffraction-limited resolution when atmospheric turbulence and photon scarcity act simultaneously. In this regime, conventional adaptive optics, speckle imaging, and blind deconvolution lack sufficient information diversity to reliably estimate either the scene or the turbulence. We present Turbulence Aware Poisson Blind Deconvolution (TAP-BD), a framework designed for robust image recovery in these extreme conditions. TAP-BD extracts more information from coded-detection through phase diversity and decodes it with a physics-informed optimization that incorporates low photon Poisson statistics. Experiments show that TAP-BD provides reliable reconstructions of both scene and turbulence using only a few tens of measurements, even under strong aberrations and photon-starved conditions where existing methods fail. This capability enables photon-efficient, turbulence resilient imaging for applications such as space situational awareness and long-range remote sensing.

physics.optics↗

arXiv2023

Telescope imaging beyond the Rayleigh limit in extremely low SNR

The Rayleigh limit and low Signal-to-Noise Ratio (SNR) scenarios pose significant limitations to optical imaging systems used in remote sensing, infrared thermal imaging, and space domain awareness. In this study, we introduce a Stochastic Sub-Rayleigh Imaging (SSRI) algorithm to localize point objects and estimate their positions, brightnesses, and number in low SNR conditions, even below the Rayleigh limit. Our algorithm adopts a maximum likelihood approach and exploits the Poisson distribution of incoming photons to overcome the Rayleigh limit in low SNR conditions. In our experimental validation, which closely mirrors practical scenarios, we focus on conditions with closely spaced sources within the sub-Rayleigh limit (0.49-1.00R) and weak signals (SNR less than 1.2). We use the Jaccard index and Jaccard efficiency as a figure of merit to quantify imaging performance in the sub-Rayleigh region. Our approach consistently outperforms established algorithms such as Richardson-Lucy and CLEAN by 4X in the low SNR, sub-Rayleigh regime. Our SSRI algorithm allows existing telescope-based optical/infrared imaging systems to overcome the extreme limit of sub-Rayleigh, low SNR source distributions, potentially impacting a wide range of fields, including passive thermal imaging, remote sensing, and space domain awareness.

physics.optics↗