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   	<dc:title>End-to-end optimization of metasurfaces for imaging with compressed sensing</dc:title>
   	<dc:creator>Arya, Gaurav</dc:creator>
   	<dc:creator>Li, William F.</dc:creator>
   	<dc:creator>Roques-Carmes, Charles</dc:creator>
   	<dc:creator>Soljačić, Marin</dc:creator>
   	<dc:creator>Johnson, Steven G.</dc:creator>
   	<dc:creator>Lin, Zin</dc:creator>
   	<dc:subject>end-to-end</dc:subject>
   	<dc:subject>optimization</dc:subject>
   	<dc:subject>metasurface</dc:subject>
   	<dc:subject>imaging</dc:subject>
   	<dc:subject>compressed sensing</dc:subject>
   	<dc:subject>ddc:530</dc:subject>
   	<dc:description>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.</dc:description>
   	<dc:publisher>American Chemical Society</dc:publisher>
   	<dc:date>2024</dc:date>
   	<dc:type>info:eu-repo/semantics/article</dc:type>
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   	<dc:type>text</dc:type>
   	<dc:type>http://purl.org/coar/resource_type/c_2df8fbb1</dc:type>
   	<dc:identifier>https://research-explorer.ista.ac.at/record/21672</dc:identifier>
   	<dc:source>Arya G, Li WF, Roques-Carmes C, Soljačić M, Johnson SG, Lin Z. End-to-end optimization of metasurfaces for imaging with compressed sensing. &lt;i&gt;ACS Photonics&lt;/i&gt;. 2024. doi:&lt;a href=&quot;https://doi.org/10.1021/acsphotonics.4c00259&quot;&gt;10.1021/acsphotonics.4c00259&lt;/a&gt;</dc:source>
   	<dc:language>eng</dc:language>
   	<dc:relation>info:eu-repo/semantics/altIdentifier/doi/10.1021/acsphotonics.4c00259</dc:relation>
   	<dc:relation>info:eu-repo/semantics/altIdentifier/e-issn/2330-4022</dc:relation>
   	<dc:relation>info:eu-repo/semantics/altIdentifier/arxiv/2201.12348</dc:relation>
   	<dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
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