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.
Authors
- Shilong Pan (ORCID: https://orcid.org/0000-0003-2620-7272)
- Ang Li (ORCID: https://orcid.org/0000-0003-4628-0864)
- Qiang Li (ORCID: https://orcid.org/0009-0007-4804-9830)
- Yue Wang (ORCID: https://orcid.org/0000-0003-0098-5359)
- Yihang Shao
- Yang Zhao
Institutions
- Nanjing University of Aeronautics and Astronautics (CN)
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
Funders
- National Natural Science Foundation of China