Plasmonic supercrystals (PSCs) made by colloidal self-assembly metallic nanoparticles can be regarded as a special kind of optical metamaterials with intriguing properties, such as engineered refractive indices and densely distributed near field “hot spots.” Analysis of the Purcell effect of PSCs is crucial for many applications related to light-emission processes, such as surface-enhanced Raman scattering and spontaneous emission enhancement. We present a detailed theoretical and numerical study on the Purcell effect of films and nanocavities made of PSCs. We first demonstrate that the spectral response of the Purcell effect of a monolayer PSC can be basically divided into the surface plasmon polariton regime, the collective plasmon (CP) regime, and the dielectric regime. In particular, we reveal that the resonances in the CP regime have rich fine structures of near fields, resulting in a strong dependence of the Purcell effect on the position and polarization of emitters. We further show that nanocavities consist of PSCs that sustain Mie-like electric and magnetic multipolar resonances that can be utilized to enhance the Purcell effect in the near-infrared band. Our results are helpful for understanding the light–matter interactions at nanoscale and may promote applications of PSCs in light-emission engineering.
Phase retrieval is one of the vital processes in many computational imaging techniques, aiming at retrieving the lost phase of light fields from the corresponding intensity. Recently, advanced deep learning strategies for phase retrieval have gained much attention mainly due to their highly efficiency and accuracy compared with conventional iterative methods represented by Gerchberg-Saxton algorithm. Here, we propose a self-supervised neural network integrated with angular spectrum transform to retrieve lost phases of color images. The network contains two complex-valued U-Nets to restore and update the light fields of the image plane and the object plane, respectively. By minimizing the difference between the reconstructed images from the network with the input images, complex light fields on the object plane can be obtained. The proposed network is able to retrieve missing phases of 200 color images within half a minute while the averaged peak signal-to-noise ratio and structural similarity of the reconstructed color images can reach 23.21 dB and 0.84, respectively. Visualization and statistic results indicate that our network is an efficient and accurate method of phase retrieval, which has potential applications in many fields of computational imaging.
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