arXiv subjects

Joseph L. Greene

Publications and source records attributed to Joseph L. Greene.

7 recordsLinked to original sources

arXiv2026

TurPy: a physics-based and differentiable optical turbulence simulator for algorithmic development and system optimization

Developing optical systems for free-space applications requires simulation tools that accurately capture turbulence-induced wavefront distortions and support gradient-based optimization. Here we introduce TurPy, a GPU-accelerated, fully differentiable wave optics turbulence simulator to bridge high fidelity simulation with end-to-end optical system design. TurPy incorporates subharmonic phase screen generation, autoregressive temporal evolution, and an automated screen placement routine balancing Fourier aliasing constraints and weak-turbulence approximations into a unified, user-ready framework. Because TurPy's phase screen generation is parameterized through a media-specific power spectral density, the framework extends to atmospheric, oceanic, and biological propagation environments with minimal modification. We validate TurPy against established atmospheric turbulence theory by matching 2nd order Gaussian beam broadening and 4th order plane wave scintillation to closed-form models with 98% accuracy across weak to strong turbulence regimes, requiring only the medium's refractive index structure constant and power spectral density as inputs. To demonstrate TurPy as a gradient-based training platform, we optimize a dual-domain diffractive deep neural network (D2NN) in a two-mask dual-domain architecture to recover a Gaussian beam from a weakly turbulent path and achieving over 58% reduction in scintillation relative to an uncompensated receiver in simulation. TurPy is released as an open-source package to support synthetic data generation, turbulence-informed algorithm development, and the end-to-end design of optical platforms operating in turbulent environments.

physics.optics↗

arXiv2026

Event-based Scheimpflug LiDAR for Ultra-Fast Laser-Scanned Rangefinding

Frame-based ranging systems are constrained by frame rate and provide no intrinsic mechanism for background rejection, limiting utility in high-throughput or cluttered environments. We present eSCHORTY, a Scheimpflug LiDAR integrating an event-based sensor with a modulated continuous-wave line laser to enable dense 3D point clouds, generated from over one million megaevents per second. We demonstrate that laser modulation provides a trade-off between event-space feature detection and localization, and that logarithmic event encoding suppresses the reflectance-induced centroid artifact demonstrated in intensity-based ranging. Reconstructions of natural scenes confirm spatially coherent depth recovery, with the Scheimpflug geometry supporting adaptation from millimeter- to kilometer-scale applications.

physics.optics↗

arXiv2026

Frequency-domain Event-based Imaging for Selective Surveillance

Event-based cameras (EBCs) are an attractive sensing modality for surveillance due to their reporting of pixel-level radiance changes with microsecond resolution and high dynamic range, enabling motion extraction while suppressing background. Their asynchronous, sparse output, however, necessitate algorithms that identify targets in event-space without processing full frames. We introduce Frequency Rate Information for Event Space (FRIES), a neuromorphic processing framework that detects periodicity in events, such as rotor rotation and mechanical vibrations, to discriminate and monitor man-made objects. FRIES first applies a time gate to suppress background and noise, then aggregates events into a pixel-wise activity (e.g., density) map and clusters pixels into regions-of-interest (ROIs). A localized spectral analysis is applied to each ROI to extract dominant frequencies used to distinguish structured object signatures from unstructured background and noise. Discriminated targets are visualized using a Resonant Time Surface (RTS), a frequency-selective method that weights events by their phase coherence with the extracted frequencies, rewarding in-sync content and suppressing out-of-sync clutter. We demonstrate FRIES and RTS in a controlled indoor experiment to recover the rotational frequency of a mechanical chopper and drone rotors against a moving background. We further test these methods on an outdoor data to detect a hovering drone against a realistic treeline. These preliminary results establish frequency-domain event processing as a promising front-end for selective surveillance in neuromorphic pipelines and a complementary surveillance modality, leveraging the high temporal resolution to enable spectral discrimination.

physics.optics↗

arXiv2026

DeepFilters: Scattering-Aware Pupil Engineering with Learned Digital Filter Reconstruction for Extended Depth of Field Microscopy

Extended depth of field microscopy encodes axial information into a single acquisition through engineered point spread functions, but conventional and deep optics approaches are subject to degradation in scattering tissue. We introduce DeepFilters, a scattering-aware deep optics framework that jointly optimizes a parameterized pupil filter and a digital-filter-based reconstruction network through a calibrated differentiable forward model to achieve broad generalization without retraining. Incorporating empirical scattering kernels, physics-guided regularization, and a hybrid genetic-gradient initialization strategy, DeepFilters extends the PSF from 16 micron to >400 micron in clear media and enables signal recovery beyond 120 micron deep in biological tissues, validated across fixed brain slices and sea urchin embryos.

physics.optics↗

arXiv2026

Monocular passive event-based range-finding of airborne objects using the Scheimpflug principle

Passive 3D sensing is increasingly critical for early detection and tracking of small aerial vehicles (UAVs), where traditional active ranging can be tactically undesirable. We present SCHeimpflug for Optical Ranging TechnologY (SCHORTY), a single-aperture passive and active ranging architecture that exploits the Scheimpflug principle to encode range along a tilted object space plane by tilting the sensor relative to the imaging optics. SCHORTY requires only a one-time geometric calibration to map pixel coordinates to range and is inherently sensor and waveband agnostic. We implement SCHORTY using both a visible frame-based camera and an event-based camera (EBC) with closely matched pixel sizes for comparable horizontal resolutions and range binning. Controlled flights of an octocopter and a fixed-wing UAV equipped with GPS provide ground truth distances out to 1.1 km. Experimental results show that SCHORTY achieves deterministic range assignment limited primarily by the projected pixel size, which grows squared distance, while avoiding computationally intensive inverse reconstructions common in coded aperture and PSF engineered systems. In the EBC configuration, EBC-SCHORTY inherently suppresses static background and emphasizes motion, improving UAV detectability in cluttered natural scenes and under turbulence and motion blur. Additionally, we observe an asymmetric defocus blur about the object plane that depends on UAV trajectory, suggesting an extra cue for localization and trajectory inference. These results demonstrate SCHORTY as a practical and Size, Weight, and Power (SWaP) efficient passive ranging solution for medium-range UAV observation and motivate future integration with 2.5D/3D PSF engineering and event-based deconvolution to enhance 3D sensing performance.

physics.optics↗

arXiv2025

Scheimpflug cameras for range-resolved observations of the atmospheric effects on laser propagation

This paper presents the development of Scheimpflug cameras for lidar and remote sensing with an emphasis on active and passive range-finding. Scheimpflug technology uses a tilted camera geometry to natively encode 3D information through projected off-axis pixel view angles and holds the unique potential to serve as an alternative to traditional lidar and remote sensing systems with the demonstrated advantages of high configurability, SWaP-C (Size, Weight and Power-Cost) efficiency and short- vs. far-range optimization. In this work, we demonstrate several compact Scheimpflug-enabled systems as a snapshot atmospheric lidar detector to measure aerosol extinction and optical turbulence effects with high precision over ranges from a few meters to a few kilometer. We compare the instrument's measurements to variance-based Cn2 data collected by a conic anemometer and scintillometer over a 50 m horizontal path. This paper also presents preliminary results on utilizing Scheimpflug technology for photogrammetry, 2D/3D mapping and includes a generalized discussion on the design, alignment and calibration procedures. We believe this work provides a strong basis for the broad use of Scheimpflug technology across multiple use-cases with the fields of lidar and remote sensing.

physics.optics↗

arXiv2025

A PyTorch-Enabled Tool for Synthetic Event Camera Data Generation and Algorithm Development

Event, or neuromorphic cameras, offer a novel encoding of natural scenes by asynchronously reporting significant changes in brightness, known as events, with improved dynamic range, temporal resolution and lower data bandwidth when compared to conventional cameras. However, their adoption in domain-specific research tasks is hindered in part by limited commercial availability, lack of existing datasets, and challenges related to predicting the impact of their nonlinear optical encoding, unique noise model and tensor-based data processing requirements. To address these challenges, we introduce Synthetic Events for Neural Processing and Integration (SENPI) in Python, a PyTorch-based library for simulating and processing event camera data. SENPI includes a differentiable digital twin that converts intensity-based data into event representations, allowing for evaluation of event camera performance while handling the non-smooth and nonlinear nature of the forward model The library also supports modules for event-based I/O, manipulation, filtering and visualization, creating efficient and scalable workflows for both synthetic and real event-based data. We demonstrate SENPI's ability to produce realistic event-based data by comparing synthetic outputs to real event camera data and use these results to draw conclusions on the properties and utility of event-based perception. Additionally, we showcase SENPI's use in exploring event camera behavior under varying noise conditions and optimizing event contrast threshold for improved encoding under target conditions. Ultimately, SENPI aims to lower the barrier to entry for researchers by providing an accessible tool for event data generation and algorithmic developmnent, making it a valuable resource for advancing research in neuromorphic vision systems.

cs.CV↗