Recent strides in data-driven and deep learning methods have empowered image and wavefront reconstruction in such environments. This breakthrough finds promising roles in biomedical applications like image transmission and holography. Yet, the reconstructed image quality relies on deep learning model effectiveness in understanding transmission mechanisms. In our presentation, we propose two enhancements. First, employs a novel deep learning architecture inspired by light physics, showcasing enhanced image reconstruction quality and broad problem generalization. The second one is an optical method which boosts data variance through holographic encoding, enabling multi-channel image transmission and improved data fusion via deep learning.
An emerging trend at the forefront of optical neural interfaces leverages the optical properties of photonic nanostructures to modulate light delivery and collection patterns in deep brain regions. This perspective article surveys the early works that have spearheaded this promising strategy, and discusses its promise towards the establishment of a class of augmented nano-neurophotonic probes.
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