End-to-End Optimization of Metasurfaces for Imaging with Compressed Sensing
Name
twgwvdtbqsbqmmcxkyycfsfnjvmtvknf.pdf
Size
7.76 MB
Format
Adobe PDF
Checksum (MD5)
a549490b314064166919e85753b19af3
Author(s)
Arya, Gaurav
Li, William F.
Roques-Carmes, Charles
Soljačić, Marin
Johnson, Steven G.
Lin, Zin
Date Issued
April 23, 2024
Journal
ACS Photonics
Publisher
American Chemical Society
Citation
ACS Photonics 2024, 11, 5, 2077–2087
Version
Author's final manuscript
Abstract
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 (e.g., the object can be described by a small number of nonzero 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 effectively optimizes 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 of dimensions. 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.
MIT Department
Massachusetts Institute of Technology. Department of Mathematics
Massachusetts Institute of Technology. Research Laboratory of Electronics
Massachusetts Institute of Technology. Department of Physics
Terms of Use
Creative Commons Attribution-Noncommercial-ShareAlike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1021/acsphotonics.4c00259
