End-to-End Optimization of Metasurfaces for Imaging with Compressed Sensing
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2022
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| _version_ | 1866911934061740032 |
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| author | Arya, Gaurav Li, William F. Roques-Carmes, Charles Soljačić, Marin Johnson, Steven G. Lin, Zin |
| author_facet | Arya, Gaurav Li, William F. Roques-Carmes, Charles Soljačić, Marin Johnson, Steven G. Lin, Zin |
| contents | We present a framework for the end-to-end optimization of metasurface imaging systems that reconstruct targets using compressed sensing, a technique for solving underdetermined imaging problems when the target object exhibits sparsity (i.e. the object can be described by a small number of non-zero values, but the positions of these values are unknown). We nest an iterative, unapproximated compressed sensing reconstruction algorithm into our end-to-end optimization pipeline, resulting in an interpretable, data-efficient method for maximally leveraging metaoptics to exploit object sparsity. We apply our framework to super-resolution imaging and high-resolution depth imaging with a phase-change material. In both situations, our end-to-end framework computationally discovers optimal metasurface structures for compressed sensing recovery, automatically balancing a number of complicated design considerations to select an imaging measurement matrix from a complex, physically constrained manifold with millions ofdimensions. The optimized metasurface imaging systems are robust to noise, significantly improving over random scattering surfaces and approaching the ideal compressed sensing performance of a Gaussian matrix, showing how a physical metasurface system can demonstrably approach the mathematical limits of compressed sensing. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2201_12348 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | End-to-End Optimization of Metasurfaces for Imaging with Compressed Sensing Arya, Gaurav Li, William F. Roques-Carmes, Charles Soljačić, Marin Johnson, Steven G. Lin, Zin Image and Video Processing Optimization and Control Optics We present a framework for the end-to-end optimization of metasurface imaging systems that reconstruct targets using compressed sensing, a technique for solving underdetermined imaging problems when the target object exhibits sparsity (i.e. the object can be described by a small number of non-zero values, but the positions of these values are unknown). We nest an iterative, unapproximated compressed sensing reconstruction algorithm into our end-to-end optimization pipeline, resulting in an interpretable, data-efficient method for maximally leveraging metaoptics to exploit object sparsity. We apply our framework to super-resolution imaging and high-resolution depth imaging with a phase-change material. In both situations, our end-to-end framework computationally discovers optimal metasurface structures for compressed sensing recovery, automatically balancing a number of complicated design considerations to select an imaging measurement matrix from a complex, physically constrained manifold with millions ofdimensions. The optimized metasurface imaging systems are robust to noise, significantly improving over random scattering surfaces and approaching the ideal compressed sensing performance of a Gaussian matrix, showing how a physical metasurface system can demonstrably approach the mathematical limits of compressed sensing. |
| title | End-to-End Optimization of Metasurfaces for Imaging with Compressed Sensing |
| topic | Image and Video Processing Optimization and Control Optics |
| url | https://arxiv.org/abs/2201.12348 |