Computed tomography using meta-optics

TL;DR

This paper introduces a meta-optic imaging system that performs the Radon transform optically, enabling high-quality image reconstruction and accurate classification without training-dependent optics, thus reducing computational load.

Contribution

The paper presents a novel meta-optic imager that implements the Radon transform directly, eliminating the need for training the optics and enabling universal applicability.

Findings

Achieved high-quality image reconstruction with 0.6% compression ratio.

Attained 90% classification accuracy on Radon dataset.

Demonstrated optical Radon transform's effectiveness for computer vision tasks.

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 metaoptic imager, which implements the Radon transform obviating the need for training the optics. High quality image reconstruction with a large compression ratio of 0.6% is presented through the use of the Simultaneous Algebraic Reconstruction Technique. Image classification with 90% accuracy is presented on an experimentally measured Radon dataset through neural network trained on digitally transformed images.