End-to-end metasurface inverse design for single-shot multi-channel imaging


Zin Lin, Raphaël Pestourie, Charles Roques-Carmes, Zhaoyi Li, Federico Capasso, Marin Soljačić, and Steven G. Johnson. 2022. “End-to-end metasurface inverse design for single-shot multi-channel imaging.” Opt. Express, 30, 16, Pp. 28358–28370. Publisher's Version
oe-30-16-28358.pdf9.59 MB


We introduce end-to-end inverse design for multi-channel imaging, in which a nanophotonic frontend is optimized in conjunction with an image-processing backend to extract depth, spectral and polarization channels from a single monochrome image. Unlike diffractive optics, we show that subwavelength-scale &\#x201C;metasurface&\#x201D; designs can easily distinguish similar wavelength and polarization inputs. The proposed technique integrates a single-layer metasurface frontend with an efficient Tikhonov reconstruction backend, without any additional optics except a grayscale sensor. Our method yields multi-channel imaging by spontaneous demultiplexing: the metaoptics front-end separates different channels into distinct spatial domains whose locations on the sensor are optimally discovered by the inverse-design algorithm. We present large-area metasurface designs, compatible with standard lithography, for multi-spectral imaging, depth-spectral imaging, and &\#x201C;all-in-one&\#x201D; spectro-polarimetric-depth imaging with robust reconstruction performance (&\#x2272; 10&\#x0025; error with 1&\#x0025; detector noise). In contrast to neural networks, our framework is physically interpretable and does not require large training sets. It can be used to reconstruct arbitrary three-dimensional scenes with full multi-wavelength spectra and polarization textures.
Last updated on 07/22/2022