Inverse design of metasurfaces for end-to-end computational imaging

Abstract

Metasurfaces, composed of two-dimensional arrays of subwavelength optical scatterers, are regarded as powerful substitutes to conventional diffractive and refractive optics. In addition, metasurfaces with powerful wavefront manipulation capabilities can steer the phase, amplitude, and polarization of light, which provides the potential to joint optimization with algorithms by encoding and decoding the light fields. In this paper, we propose an end-to-end computational imaging system which is joint optimized of metaoptics and neural networks based on the designed initial phase. We construct the forward model of the unit cell to the optical response and the inverse mapping of the optical response to the unit cell for the differentiable front-end metaoptics. Based on the appropriate initial phase, the calculation of the framework would converge faster, and the proposed system will promote the further development of metaoptics and computational imaging.

End-to-end metasurface inverse design for NIR computational imaging

“…Computational imaging encompasses two key components: front-end optical elements that serve as encoders of light field information, and back-end imaging recovery algorithms that function as decoders of light field information 1 . Notably, the approach of jointly optimizing the encoders and decoders in an end-to-end manner exhibits greater potential compared to separate optimization of optics and algorithms [1][2][3][4][5][6][7][8] . Metasurfaces, which consist of subwavelength and ultrathin resonators arranged in an array, have garnered significant attention due to their remarkable ability to manipulate the amplitude, phase, and polarization state of wavefronts in a versatile manner [9][10][11][12][13] .…”