Generation of Photonic Hooks under Point-Source Illumination from Patchy Microcylinders

Photonic hook (PH) is a new type of non-evanescent light beam with subwavelength curved structures. It has shown promising applications in super-resolution imaging and has the potential to be used in micromachining, optical trapping, etc. PHs are generally produced by illuminating mesoscale asymmetric particles with optical plane waves. In this work, we used the finite-difference time-domain (FDTD) method to investigate the PH phenomenon under point-source illumination. We found that the PHs can be effectively generated from point-source illuminated patchy particles. By changing the background refractive index, particle diameters and the position and coverage ratio of Ag patches, the characteristics of the PHs can be effectively tuned. Moreover, the structure of the intensity distribution of the light field generated from small and large particles can have an opposite bending direction due to the near-field light-matter interaction

ContransGAN: Convolutional Neural Network Coupling Global Swin-Transformer Network for High-Resolution Quantitative Phase Imaging with Unpaired Data

Optical quantitative phase imaging (QPI) is a frequently used technique to recover biological cells with high contrast in biology and life science for cell detection and analysis. However, the quantitative phase information is difficult to directly obtain with traditional optical microscopy. In addition, there are trade-offs between the parameters of traditional optical microscopes. Generally, a higher resolution results in a smaller field of view (FOV) and narrower depth of field (DOF). To overcome these drawbacks, we report a novel semi-supervised deep learning-based hybrid network framework, termed ContransGAN, which can be used in traditional optical microscopes with different magnifications to obtain high-quality quantitative phase images. This network framework uses a combination of convolutional operation and multiheaded self-attention mechanism to improve feature extraction, and only needs a few unpaired microscopic images to train. The ContransGAN retains the ability of the convolutional neural network (CNN) to extract local features and borrows the ability of the Swin-Transformer network to extract global features. The trained network can output the quantitative phase images, which are similar to those restored by the transport of intensity equation (TIE) under high-power microscopes, according to the amplitude images obtained by low-power microscopes. Biological and abiotic specimens were tested. The experiments show that the proposed deep learning algorithm is suitable for microscopic images with different resolutions and FOVs. Accurate and quick reconstruction of the corresponding high-resolution (HR) phase images from low-resolution (LR) bright-field microscopic intensity images was realized, which were obtained under traditional optical microscopes with different magnifications

Generation of Photonic Hooks under Point-Source Illumination from Patchy Microcylinders

Photonic hook (PH) is a new type of non-evanescent light beam with subwavelength curved structures. It has shown promising applications in super-resolution imaging and has the potential to be used in micromachining, optical trapping, etc. PHs are generally produced by illuminating mesoscale asymmetric particles with optical plane waves. In this work, we used the finite-difference time-domain (FDTD) method to investigate the PH phenomenon under point-source illumination. We found that the PHs can be effectively generated from point-source illuminated patchy particles. By changing the background refractive index, particle diameters and the position and coverage ratio of Ag patches, the characteristics of the PHs can be effectively tuned. Moreover, the structure of the intensity distribution of the light field generated from small and large particles can have an opposite bending direction due to the near-field light-matter interaction

Visualization 1: Simple calculation of a computer-generated hologram for lensless holographic 3D projection using a nonuniform sampled wavefront recording plane

Visualization 1 shows the continuous accommodation depth cue in numerical experiments. Originally published in Applied Optics on 01 October 2016 (ao-55-28-7988