Computed Tomography Using Meta-Optics

Abstract

Computer vision tasks require processing large amounts of data to perform image classification, segmentation, and feature extraction. Optical preprocessors can potentially reduce the number of floating-point operations required by computer vision tasks, enabling low-power and low-latency operation. However, existing optical preprocessors are mostly learned and hence strongly depend on the training data and thus lack universal applicability. In this paper, we present a meta-optic imager, which implements the Radon transform, obviating the need for training the optics. High-quality image reconstruction with a large compression ratio of 9.2% is presented through the use of the simultaneous algebraic reconstruction technique. We also demonstrate image classification with 90% accuracy on a further compressed (0.6% of total measured pixels) Radon data set through a neural network trained on digitally transformed images. Our work shows the efficacy of data-independent encoding in an optical encoder. While our platform is based on meta-optics, we note that such encoding can be performed with other optics as well.