Author

Kaiwei Wang

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Preprint Jul 2026

Real-Time Megapixel Kilohertz Neuromorphic Shack-Hartmann Wavefront Sensor

Conventional frame-based Shack--Hartmann wavefront sensors (SHWFS) are limited by dynamic range and the intrinsic trade-off between spatial and temporal resolution, while high-bandwidth acquisition poses additional challenges for real-time wavefront reconstruction. This work presents a real-time, megapixel, kilohertz neuromorphic SHWFS to overcome these limitations. In static optical metrology, the proposed pipeline achieves one-shot wavefront acquisition under extreme illumination non-uniformity, reaching a dynamic range of 260 dB at a 20 Hz acquisition frequency. Owing to this high dynamic range and the concomitant high-intensity resolution, wavefront reconstruction errors in dim and bright sub-apertures are reduced by 59\% and 70\%, respectively, relative to conventional frame-based SHWFS. For dynamic wavefront sensing, the system provides kilohertz-rate centroid tracking over a megapixel field of view with microsecond-scale latency. Centroid localization errors are 0.18 pixels during optical alignment supervision and 0.26 pixels in high-speed turbulence observation, verifying the accuracy and reliability of the system across dynamic scenarios. The per-sub-aperture processing throughput reaches 420,737 Hz on a standard CPU, demonstrating high-speed real-time computation without specialized hardware acceleration. Together, these results establish a unified neuromorphic SHWFS framework for high-fidelity one-shot static wavefront reconstruction and real-time high-bandwidth dynamic wavefront sensing.

Yuhan Bao, Chenxin Shao, Kai-Wei Wang · 0 citations

Preprint Aug 2026

Event-Based Motion Estimation via Oriented Distance Fields

Event-based motion estimation is central to tasks that demand high temporal resolution and robustness to fast motion. Existing methods typically rely on iterative optimization or repeated hypothesis comparison, offsetting the sensor's low-latency advantage. We propose Oriented Distance Field Motion Estimation (ODF Motion Estimation), which replaces this optimization with a single averaging step over a precomputed field of event distance vectors, combined with an adaptive event-count selection strategy and a parameter-free trail filter. On public and self-collected datasets, ODF motion estimation reaches sub-pixel accuracy at the lowest latency among compared methods. We validate its generality on two downstream applications rather than treating them as separate contributions. First, the estimated trajectory is converted into a blur kernel and paired with a compact iterative-unfolding network, trained on simulated motion-estimation noise, for real-time non-blind image deblurring, attaining competitive or superior PSNR/SSIM with under 1M parameters. Second, the same precomputed field is repurposed for directional event filtering in a low-power asynchronous pupil and glint tracker, sustaining stable tracking for tens of seconds while lowering a near-eye module's power draw.

Lei Sun, Yuqin Ma, Weilun Li et al. · 0 citations