IDEAL-IO decouples density estimation from optical optimization to make information-theoretic imaging design practical, cutting runtime and memory by up to 6x while enabling more expressive density models.
End-to-End Nanophotonic Inverse Design for Imaging and Polarimetry
1 Pith paper cite this work. Polarity classification is still indexing.
abstract
By co-designing a meta-optical front end in conjunction with an image-processing back end, we demonstrate noise sensitivity and compactness substantially superior to either an optics-only or a computation-only approach, illustrated by two examples: subwavelength imaging and reconstruction of the full polarization coherence matrices of multiple light sources. Our end-to-end inverse designs couple the solution of the full Maxwell equations---exploiting all aspects of wave physics arising in subwavelength scatterers---with inverse-scattering algorithms in a single large-scale optimization involving $\gtrsim 10^4$ degrees of freedom. The resulting structures scatter light in a way that is radically different from either a conventional lens or a random microstructure, and suppress the noise sensitivity of the inverse-scattering computation by several orders of magnitude. Incorporating the full wave physics is especially crucial for detecting spectral and polarization information that is discarded by geometric optics and scalar diffraction theory.
citation-role summary
citation-polarity summary
fields
eess.IV 1years
2025 1verdicts
CONDITIONAL 1roles
background 1polarities
background 1representative citing papers
citing papers explorer
-
Computationally Efficient Information-Driven Optical Design with Interchanging Optimization
IDEAL-IO decouples density estimation from optical optimization to make information-theoretic imaging design practical, cutting runtime and memory by up to 6x while enabling more expressive density models.