论文标题
基于针的深神经网络摄像头
A needle-based deep-neural-network camera
论文作者
论文摘要
我们在实验上展示了一台摄像机，其主要视频是套管（直径= 0.22mm，长度= 12.5mm），该摄像头可作用于从物体平面（35厘米）到其相对端的光柱运输光强度。深神经网络（DNN）用于重建颜色和灰度图像，其视野为180，角度分辨率约为0.40。当对具有深度信息的图像进行培训时，DNN可以创建深度图。最后，我们在没有图像重建的情况下显示了基于DNN的EMNIST数据集的分类。前者对于具有增强隐私的成像可能很有用。
We experimentally demonstrate a camera whose primary optic is a cannula (diameter=0.22mm and length=12.5mm) that acts a lightpipe transporting light intensity from an object plane (35cm away) to its opposite end. Deep neural networks (DNNs) are used to reconstruct color and grayscale images with field of view of 180 and angular resolution of ~0.40. When trained on images with depth information, the DNN can create depth maps. Finally, we show DNN-based classification of the EMNIST dataset without and with image reconstructions. The former could be useful for imaging with enhanced privacy.
