Needle-Based Deep-Neural-Network Camera

We experimentally demonstrate a camera whose primary optic is a cannula/needle (diameter = 0.22 mm and length = 12.5 mm) 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 degrees and angular resolution of similar to 0.4 degrees. We showed a large effective demagnification of 127 x. Most interestingly, we showed that such a camera could achieve dose to diffraction-limited performance with an effective numerical aperture of 0.045, depth of focus similar to 16 mu m and resolution dose to the sensor pixel size (3.2 mu m). 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. (C) 2021 Optical Society of America under the terms of the OSA Open Access Publishing Agreement