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.

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 20x 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.

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.