Neural-Network-Enhanced Metalens Camera for High-Definition, Dynamic Imaging in the Long-Wave Infrared Spectrum

Abstract

To provide a lightweight and cost-effective solution for long-wave infrared imaging using a singlet, we developed a neural network-enhanced metalens camera by integrating a high-frequency-enhancing (HFE) cycle-GAN neural network into a metalens imaging system. The HFE cycle-GAN improves the quality of the original metalens images by addressing inherent frequency loss introduced by the metalens. In addition to the bidirectional cyclic generative adversarial network, it incorporates a high-frequency adversarial learning module. This module utilizes wavelet transform to extract high-frequency components and then establishes a high-frequency feedback loop. It enables the generator to enhance the camera outputs by integrating adversarial feedback from the high-frequency discriminator. This ensures that the generator adheres to the constraints imposed by the high-frequency adversarial loss, thereby effectively recovering the camera’s frequency loss. This recovery guarantees high-fidelity image output from the camera, facilitating smooth video production. Our neural-network-enhanced metalens camera is capable of achieving dynamic imaging at 125 frames per second with an end point error value of 12.58. We also achieved 0.42 for the Fréchet inception distance, 30.62 for the peak signal to noise ratio, and 0.69 for structural similarity in the recorded videos.