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

TL;DR

This paper presents a neural-network-enhanced metalens camera system for high-definition, dynamic long-wave infrared imaging, utilizing a Cycle-GAN with high-frequency feedback to improve image quality and recover frequency loss.

Contribution

It introduces a novel integration of a High-Frequency-Enhancing Cycle-GAN with a metalens system to enhance infrared image quality and enable real-time dynamic imaging.

Findings

Achieves 125 fps imaging rate.

Attains a Fréchet Inception Distance of 0.42.

Records high PSNR and SSIM scores.

Abstract

To provide a lightweight and cost-effective solution for the long-wave infrared imaging using a singlet, we develop a camera by integrating a High-Frequency-Enhancing Cycle-GAN neural network into a metalens imaging system. The High-Frequency-Enhancing 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 camera is capable of achieving dynamic imaging at 125 frames per second with an End Point Error value of 12.58. We also achieve 0.42 for Fr\'echet Inception Distance, 30.62 for Peak Signal to Noise Ratio, and 0.69 for Structural Similarity in the recorded videos.