Photonic computing enabled end-to-end image compression and reconstruction with adjustable ratio and high-fidelity

The rapid development of remote sensing, satellite radar, and medical equipment has created an imperative demand for ultra-efficient image compression and reconstruction. We demonstrate an end-to-end image compression and reconstruction approach using an opto-electronic computing processor, achieving orders-of-magnitude higher speed and lower energy consumption than electronic counterparts. Its core is a 32×32 silicon photonic computing chip, which monolithically integrates 32 high-speed modulators, 32 detectors, and a programmable photonic matrix core, co-packaged with all necessary control electronics. Leveraging the photonic core's programmability, the processor generates trainable compressive matrices, enabling adjustable image compression ratios (up to 256×) to meet diverse application needs. Deploying a customized lightweight photonic integrated circuit-oriented network enables high-quality reconstruction of compressed images. Our approach core parts require end-to-end latency of 49.5 ps/pixel while consuming less than 10.6nJ/pixel. This work not only provides a transformative solution for computational image processing but also opens new avenues for photonic computing application.

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Publication Details

Journal
Nature Communications
Published
2026-04-27
DOI
https://doi.org/10.1038/s41467-026-71296-0
Primary Topic
Neural Networks and Reservoir Computing
Type
article
Field-Weighted Citation Impact
0.00

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Photonic computing enabled end-to-end image compression and reconstruction with adjustable ratio and high-fidelity

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Photonic computing enabled end-to-end image compression and reconstruction with adjustable ratio and high-fidelity

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Abstract

The rapid development of remote sensing, satellite radar, and medical equipment has created an imperative demand for ultra-efficient image compression and reconstruction. We demonstrate an end-to-end image compression and reconstruction approach using an opto-electronic computing processor, achieving orders-of-magnitude higher speed and lower energy consumption than electronic counterparts. Its core is a 32×32 silicon photonic computing chip, which monolithically integrates 32 high-speed modulators, 32 detectors, and a programmable photonic matrix core, co-packaged with all necessary control electronics. Leveraging the photonic core's programmability, the processor generates trainable compressive matrices, enabling adjustable image compression ratios (up to 256×) to meet diverse application needs. Deploying a customized lightweight photonic integrated circuit-oriented network enables high-quality reconstruction of compressed images. Our approach core parts require end-to-end latency of 49.5 ps/pixel while consuming less than 10.6nJ/pixel. This work not only provides a transformative solution for computational image processing but also opens new avenues for photonic computing application.

Nature Communications
Nanjing University of Aeronautics and Astronautics (CN)
National Natural Science Foundation of China
Affordable and clean energy
Openalex Percentile: Top 4%
Neural Networks and Reservoir Computing
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