Wide-field, high-resolution reconstruction in computational multi-aperture miniscope using a Fourier neural network.

ABSTRACT: Traditional fluorescence microscopy is constrained by inherent trade-offs among resolution, field of view, and system complexity. To navigate these challenges, we introduce a simple and low-cost computational multi-aperture miniature microscope, utilizing a microlens array for single-shot wide-field, high-resolution imaging. Addressing the challenges posed by extensive view multiplexing and non-local, shift-variant aberrations in this device, we present SV-FourierNet, a multi-channel Fourier neural network. SV-FourierNet facilitates high-resolution image reconstruction across the entire imaging field through its learned global receptive field. We establish a close relationship between the physical spatially varying point-spread functions and the network's learned effective receptive field.

SUBMITTER: Yang Q 

PROVIDER: S-EPMC11784641 | biostudies-literature | 2024 Jun

REPOSITORIES: biostudies-literature