Computed tomography using meta-optics
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866910697195044864 |
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| author | Zhelyeznuyakov, Maksym Fröch, Johannes E. Colburn, Shane Brunton, Steven L. Majumdar, Arka |
| author_facet | Zhelyeznuyakov, Maksym Fröch, Johannes E. Colburn, Shane Brunton, Steven L. Majumdar, Arka |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_08995 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Computed tomography using meta-optics Zhelyeznuyakov, Maksym Fröch, Johannes E. Colburn, Shane Brunton, Steven L. Majumdar, Arka Computer Vision and Pattern Recognition Optics 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. |
| title | Computed tomography using meta-optics |
| topic | Computer Vision and Pattern Recognition Optics |
| url | https://arxiv.org/abs/2411.08995 |