# Computed tomography using meta-optics

Source: [Computed tomography using meta-optics](<https://www.sota2.com/research/paper/68fa48871e209131ce930526>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T06:53:40.645Z

Authors: Maksym Zhelyeznuyakov, Johannes E. Fr\\"och, Shane Colburn, Steven L. Brunton, Arka Majumdar

Published: 2024-11-13T19:34:10.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2411.08995>)

[PDF](<https://arxiv.org/pdf/2411.08995.pdf>)