@article{21672,
  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 (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.},
  author       = {Arya, Gaurav and Li, William F. and Roques-Carmes, Charles and Soljačić, Marin and Johnson, Steven G. and Lin, Zin},
  issn         = {2330-4022},
  journal      = {ACS Photonics},
  keywords     = {end-to-end, optimization, metasurface, imaging, compressed sensing},
  publisher    = {American Chemical Society},
  title        = {{End-to-end optimization of metasurfaces for imaging with compressed sensing}},
  doi          = {10.1021/acsphotonics.4c00259},
  year         = {2024},
}