Streaming quanta sensors for online, high-performance imaging and vision
This enables real-time imaging and vision applications for QIS, which are currently limited by data and processing bottlenecks, though it is incremental as it builds on existing sensor technology.
The paper tackles the high data rates and computational demands of quanta image sensors (QIS) by introducing a low-bandwidth streaming representation, achieving real-time video reconstruction at 10-30 fps with ~100X bandwidth reduction and 10^4-10^5 times less computation than state-of-the-art methods while maintaining quality.
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.