A needle-based deep-neural-network camera

Abstract

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.

References in corpus (9)

• Non-invasive real-time imaging through scattering layers and around corners via speckle correlations
• EMNIST: an extension of MNIST to handwritten letters
• ResNet with one-neuron hidden layers is a Universal Approximator
• Lensless Photography with only an image sensor
• Towards Principled Design of Deep Convolutional Networks: Introducing SimpNet
• Lensless-camera based machine learning for image classification
• Computational Cannula Microscopy of neurons using Neural Networks
• 3D Computational Cannula Fluorescence Microscopy enabled by Artificial Neural Networks
• Optics-free imaging of complex, non-sparse QR-codes with Deep Neural Networks