Thomas Matthew, Kumar Santosh, Huang Yu-Ping
Opt Lett. 2023 Aug 15;48(16):4320-4323. doi: 10.1364/OL.498296.
We propose and experimentally demonstrate a novel, to the best of our knowledge, hybrid optoelectronic system that utilizes mode-selective frequency upconversion, single-pixel detection, and a deep neural network to achieve the reliable reconstruction of two-dimensional (2D) images from a noise-contaminated database of handwritten digits. Our system is designed to maximize the multi-scale structural similarity index measure (MS-SSIM) and minimize the mean absolute error (MAE) during the training process. Through extensive evaluation, we have observed that the reconstructed images exhibit high-quality results, with a peak signal-to-noise ratio (PSNR) reaching approximately 20 dB and a structural similarity index measure (SSIM) of around 0.85. These impressive metrics demonstrate the effectiveness and fidelity of our image reconstruction technique. The versatility of our approach allows its application in various fields, including Lidar, compressive imaging, volumetric reconstruction, and so on.
据我们所知，我们提出并通过实验证明了一种新型的混合光电系统，该系统利用模式选择性频率上转换、单像素检测和深度神经网络，从受噪声污染的手写数字数据库中可靠地重建二维（2D）图像。我们的系统旨在在训练过程中最大化多尺度结构相似性指数测量（MS-SSIM）并最小化平均绝对误差（MAE）。通过广泛评估，我们观察到重建图像呈现出高质量的结果，峰值信噪比（PSNR）达到约20 dB，结构相似性指数测量（SSIM）约为0.85。这些令人印象深刻的指标证明了我们图像重建技术的有效性和保真度。我们方法的通用性使其能够应用于各个领域，包括激光雷达、压缩成像、体积重建等等。
