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

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. This ensures that SV-FourierNet has effectively encapsulated the spatially varying aberrations in our system and learned a physically meaningful function for image reconstruction. Training of SV-FourierNet is conducted entirely on a physics -based simulator. We showcase wide -field, high -resolution video reconstructions on colonies of freely moving C. elegans and imaging of a mouse brain section. Our computational multi -aperture miniature microscope, augmented with SV-FourierNet, represents a major advancement in computational microscopy and may find broad applications in biomedical research and other fields requiring compact microscopy solutions. (c) 2024 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement