Appl Opt. 2021 Apr 1;60(10):B135-B140. doi: 10.1364/AO.415059.
We experimentally demonstrate a camera whose primary optic is a cannula/needle (=0.22 and =12.5) that acts as a light pipe transporting light intensity from an object plane (35 cm away) to its opposite end. Deep neural networks (DNNs) are used to reconstruct color and grayscale images with a field of view of 18° and angular resolution of ∼0.4. We showed a large effective demagnification of 127×. Most interestingly, we showed that such a camera could achieve close to diffraction-limited performance with an effective numerical aperture of 0.045, depth of focus ∼16µ, and resolution close to the sensor pixel size (3.2 µm). When trained on images with depth information, the DNN can create depth maps. Finally, we show DNN-based classification of the EMNIST dataset before and after image reconstructions. The former could be useful for imaging with enhanced privacy.
我们实验演示了一种相机，其主光学器件是一根套管/针（=0.22 和 =12.5），用作光管，将光强度从物平面（35 厘米远）传输到其相对端。深度神经网络 (DNN) 用于重建具有 18°视场和 ∼0.4 角分辨率的彩色和灰度图像。我们展示了 127×的大有效放大率。最有趣的是，我们展示了这样的相机可以实现接近衍射极限的性能，有效数值孔径为 0.045，焦深约 16µ，分辨率接近传感器像素大小（3.2 µm）。当在具有深度信息的图像上进行训练时，DNN 可以创建深度图。最后，我们展示了基于 DNN 的 EMNIST 数据集的分类，包括图像重建前后的分类。前者可用于增强隐私性的成像。
