论文标题
通过光纤进行超分辨率成像的生成对抗网络
Generative adversarial network for super-resolution imaging through a fiber
论文作者
论文摘要
多模纤维代表成像内窥镜小型化的最终极限。在这里，我们提出了一种使用数据驱动的机器学习框架采用压缩感测的光纤成像方法。我们在不依赖样本稀疏性约束的情况下实现了一个生成的对抗网络进行图像重建。所提出的方法在图像质量和噪声稳健性方面优于常规压缩成像算法。我们在实验中通过多模纤维在亚nyquist速度下以低于衍射极限为基于斑点的成像。
A multimode fiber represents the ultimate limit in miniaturization of imaging endoscopes. Here we propose a fiber imaging approach employing compressive sensing with a data-driven machine learning framework. We implement a generative adversarial network for image reconstruction without relying on a sample sparsity constraint. The proposed method outperforms the conventional compressive imaging algorithms in terms of image quality and noise robustness. We experimentally demonstrate speckle-based imaging below the diffraction limit at a sub-Nyquist speed through a multimode fiber.
