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

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.