Streaming quanta sensors for online, high-performance imaging and vision

TL;DR

This paper introduces a low-bandwidth streaming computational pipeline for quanta image sensors, enabling real-time high-performance imaging and vision with significant data reduction and computational efficiency.

Contribution

It presents a novel streaming representation and neural network-based reconstruction method for QIS, achieving real-time processing with minimal data and computational costs.

Findings

Achieves ~100X data bandwidth reduction

Enables real-time video reconstruction at 10-30 fps

Reduces computational load by 10^4 to 10^5 times compared to existing methods

Abstract

Recently quanta image sensors (QIS) -- ultra-fast, zero-read-noise binary image sensors -- have demonstrated remarkable imaging capabilities in many challenging scenarios. Despite their potential, the adoption of these sensors is severely hampered by (a) high data rates and (b) the need for new computational pipelines to handle the unconventional raw data. We introduce a simple, low-bandwidth computational pipeline to address these challenges. Our approach is based on a novel streaming representation with a small memory footprint, efficiently capturing intensity information at multiple temporal scales. Updating the representation requires only 16 floating-point operations/pixel, which can be efficiently computed online at the native frame rate of the binary frames. We use a neural network operating on this representation to reconstruct videos in real-time (10-30 fps). We illustrate why such representation is well-suited for these emerging sensors, and how it offers low latency and high frame rate while retaining flexibility for downstream computer vision. Our approach results in significant data bandwidth reductions ~100X and real-time image reconstruction and computer vision -- $10^4$-$10^5$ reduction in computation than existing state-of-the-art approach while maintaining comparable quality. To the best of our knowledge, our approach is the first to achieve online, real-time image reconstruction on QIS.