Implicit neural representation based on optoelectronic periodic nonlinear activation

Implicit neural representation (INR) networks break through the accuracy and resolution limitations of traditional discrete representations by modeling high-dimensional data as continuously differentiable implicit neural networks, enabling lossless compression and efficient reconstruction of details in a compact form. However, an optical-assisted INR network has yet to be demonstrated. INR networks require high nonlinearity, whereas implementing analog nonlinear activation in photonic neural networks is a challenge. Inspired by the inherent physical properties of modulators, we propose an optoelectronic nonlinear activation and implement it on the image reconstruction task. Simulations and experiments demonstrate that the proposed optoelectronic periodic neural network can represent images and perform image reconstruction with excellent results. This approach empowers complex image reconstruction with high-frequency details and reduces the amount of required hardware. Our method enables the development of compact, efficient optoelectronic neural networks, utilizing repeatable modular units for scalable and practical high-performance computing. It can enable scene generation and compression in biomedicine, autonomous driving, and augmented reality/virtual reality. © The Authors. Published by SPIE and CLP under a Creative Commons Attribution 4.0 International License.