用神经网络修复金属透镜红外成像的模糊问题，实现高速高清动态拍摄。
Neural-Network-Enhanced Metalens Camera for High-Definition, Dynamic Imaging in the Long-Wave Infrared Spectrum
- 通过高频对抗生成网络增强金属透镜成像的细节
- 实测达125帧/秒，图像保真度误差仅12.58
- 适合需要轻量化红外成像的科研与工业场景
为在长波红外谱段实现轻量级、低成本的单透镜成像，我们构建了一种集成高频增强循环生成对抗网络（High-Frequency-Enhancing Cycle-GAN）的金属透镜相机系统。该网络通过小波变换提取高频成分，并引入高频对抗学习模块与反馈回路，使生成器在高频判别器的约束下有效恢复金属透镜导致的固有频率损失。实验表明，该系统可实现125帧/秒的动态成像，端点误差（End Point Error）为12.58，弗雷歇入学距离（Fréchet Inception Distance）为0.42，峰值信噪比（Peak Signal to Noise Ratio）为30.62，结构相似性（Structural Similarity）为0.69，保障了高保真视频输出。
原文摘要 · Abstract (English)
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 Fréchet Inception Distance, 30.62 for Peak Signal to Noise Ratio, and 0.69 for Structural Similarity in the recorded videos.
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