@article{fc43b5b4840b45b580ef35895ce742ed,
title = "Snapshot ptychography on array cameras",
abstract = "We use convolutional neural networks to recover images optically down-sampled by 6.7 × using coherent aperture synthesis over a 16 camera array. Where conventional ptychography relies on scanning and oversampling, herewe apply decompressive neural estimation to recover full resolution image from a single snapshot, although as shown in simulation multiple snapshots can be used to improve signal-to-noise ratio (SNR). In place training on experimental measurements eliminates the need to directly calibrate the measurement system. We also present simulations of diverse array camera sampling strategies to explore how snapshot compressive systems might be optimized.",
author = "Chengyu Wang and Minghao Hu and Yuzuru Takashima and Schulz, {Timothy J.} and Brady, {David J.}",
note = "Funding Information: achieved by adjusting the illumination angle. One can consider adding extra illumination sources oWr ienshtaavlelinshgotwhne tlhaaset irtoisnpaotsrsainbslelatotocr.ombinecoherent image data over multiple camera apertures to super-resolve a remote scene with a single snapshot of data. Of course, our system is contrived in the sense that we have full control over the object field through an SLM, which allows us to 5t.rainCtohnecsylustsemionwithoutfully calibrating the structure of the forward model. In future work, we hope to build on the results presented here to create synthetic aperture images of natural objects. We have shown that it is possible to combine coherent image data over multiple camera apertures We imagine that such an imaging system can be calibrated with a combination of structured to super-resolve a remote scene with a single snapshot of data. Of course, our system is contrived have also compared diverse array structures and found that unstructured arrays perform best with snapshot reconstruction. Again referring to future work, we anticipate that multiframe estimation hopeovertomobvuilding ponlattheformsresultswill presentedfurther imphererovetothesecreateresults.synthetic aperture images of natural objects. We imagine that such an imaging system can be calibrated with a combination of structured Funding. ThismaterialisbaseduponworksupportedbytheDefenseAdvancedResearchProjectAgency illumination and test objects, but we leave demonstration of such calibration to future work. We under Grant No. N66001-21-1-4030. have also compared diverse array structures and found that unstructured arrays perform best with snapshot reconstruction. Again referring to future work, we anticipate that multiframe estimation ovDeartamaovvaiinlagbpillitayt.forDmastawuinlldfeurlyrtihnegrthime pexrpoevreimtheenstaelrreessuullttss.presented in this paper are available in Ref.[44]. DataunderlyingthesimulationresultspresentedinthispaperareavailableinRef.[46,47]. Funding. Defense Advanced Research Projects Agency (N66001-21-1-4030). Supplemental document. See Supplement 1 for supporting content. Publisher Copyright: {\textcopyright} 2022 Optica Publishing Group.",
year = "2022",
month = jan,
day = "17",
doi = "10.1364/OE.447499",
language = "English (US)",
volume = "30",
pages = "2585--2598",
journal = "Optics Express",
issn = "1094-4087",
publisher = "The Optical Society",
number = "2",
}