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.
In this work, a novel transparent surface-enhanced Raman scattering (SERS) for the application of probe sensing is presented. This SERS is made by a two-dimensional array of noble metal which contains nano bowls with scattered nanospheres on its surface. Using the theory of transformation optics, we show that the curvature of nano bowls amplify the electric field around the nanospheres. This amplification is broadband due to the inherent nature of space transformation which does not rely on frequency. Comparisons with conventional flat SERS are done to demonstrate the advantages of the present design. We show that the curvature of these nano bowls increases the volume of the hotspot by one order of magnitude. This significantly reduces the response time of the SERS. Also, it is shown that this curvature amplifies the electric field in hotspot more than hundreds of times greater than SERS without using those nano bowls. The calculated amplification of the Raman signal is more than one billion times so this surface is a promising candidate for single molecule detection. The optimization and simulations are done using the Finite Element numerical algorithm.
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