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arXiv · 2306.04554

Integrated Photonic Encoder for Terapixel Image Processing

Xiao Wang·Brandon Redding·Nicholas Karl·Christopher Long·Zheyuan Zhu·Shuo Pang·David Brady·Raktim Sarma
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Abstract

Modern lens designs are capable of resolving >10 gigapixels, while advances in camera frame-rate and hyperspectral imaging have made Terapixel/s data acquisition a real possibility. The main bottlenecks preventing such high data-rate systems are power consumption and data storage. In this work, we show that analog photonic encoders could address this challenge, enabling high-speed image compression using orders-of-magnitude lower power than digital electronics. Our approach relies on a silicon-photonics front-end to compress raw image data, foregoing energy-intensive image conditioning and reducing data storage requirements. The compression scheme uses a passive disordered photonic structure to perform kernel-type random projections of the raw image data with minimal power consumption and low latency. A back-end neural network can then reconstruct the original images with structural similarity exceeding 90%. This scheme has the potential to process Terapixel/s data streams using less than 100 fJ/pixel, providing a path to ultra-high-resolution data and image acquisition systems.

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Xiao Wang, Brandon Redding, Nicholas Karl, Christopher Long, Zheyuan Zhu, Shuo Pang, David Brady, Raktim Sarma. 2023-06-07. Integrated Photonic Encoder for Terapixel Image Processing. https://arxiv.org/abs/2306.04554

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