Large depth-of-field ultra-compact microscope by progressive optimization and deep learning.

ABSTRACT: The optical microscope is customarily an instrument of substantial size and expense but limited performance. Here we report an integrated microscope that achieves optical performance beyond a commercial microscope with a 5×, NA 0.1 objective but only at 0.15 cm³ and 0.5 g, whose size is five orders of magnitude smaller than that of a conventional microscope. To achieve this, a progressive optimization pipeline is proposed which systematically optimizes both aspherical lenses and diffractive optical elements with over 30 times memory reduction compared to the end-to-end optimization. By designing a simulation-supervision deep neural network for spatially varying deconvolution during optical design, we accomplish over 10 times improvement in the depth-of-field compared to traditio

SUBMITTER: Zhang Y 

PROVIDER: S-EPMC10336131 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature