Author

Matthew R. Ziemann

2 papers indexed here

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Oct 2026

Event-based Shack–Hartmann wavefront sensing using spiking neural networks

This work introduces a spiking neural network framework for wavefront reconstruction from event-based Shack–Hartmann data, which offers a significantly reduced algorithmic computational cost compared with a CNN, and offers a roadmap towards improved neuromorphic adaptive optics.

Divya Koganti, Matthew R. Ziemann, Christopher A. Metzler · Unconventional Imaging, Sens... · 0 citations

Preprint Aug 2026

Implicit Neural Speckle Denoising

This work introduces a training-free framework that combines a spatiotemporal implicit neural representation with an aperture-aware maximum-likelihood formulation to recover dynamic, speckle-free imagery directly from noisy observations, enabling adaptation to arbitrary pupil geometries without retraining.

Matthew R. Ziemann, Casey J. Pellizzari, Tyler J. Hardy et al. · 0 citations