High-Resolution Metalens Imaging with Sequential Artificial Intelligence Models.

An analysis of the optical response of a GaN-based metalens was conducted alongside the utilization of two sequential artificial intelligence (AI) models in addressing the occasional issues of blurriness and color cast in captured images. The optical loss of the metalens in the blue spectral range was found to have resulted in the color cast of images. Autoencoder and CodeFormer sequential models were employed in order to correct the color cast and reconstruct image details, respectively. Said sequential models successfully addressed the color cast and reconstructed details for all of the allocated face image categories. Subsequently, the CIE 1931 chromaticity diagrams and peak signal-to-noise ratio analysis provided numerical evidence of the AI models' effectiveness in image reconstruction. Furthermore, the AI models can still repair the image without blue information. Overall, the integration of metalens and artificial intelligence models marks a breakthrough in enhancing the performance of full-color metalens-based imaging systems.

Microscope autofocus algorithm based on number of image slope variations.

This paper presents a passive autofocus algorithm applicable to interferometric microscopes. The proposed algorithm uses the number of slope variations in an image mask to locate the focal plane (based on focus-inflection points) and identify the two neighboring planes at which fringes respectively appear and disappear. In experiments involving a Mirau objective lens, the proposed algorithm matched the autofocusing performance of conventional algorithms, and significantly outperformed detection schemes based on zero-order interference fringe in dealing with all kinds of surface blemish, regardless of severity. In experiments, the proposed algorithm also proved highly effective in cases without fringes.

Developing a microspectrophotometer to measure the dependence of broadband refractive indices on Ge-doped concentrations in GRIN rods.

A confocal microspectrophotometer is utilized to scan the surface reflectivities of a polished gradient-index (GRIN) rod in the range of 400 to 900 nm. The pure fused silica is used to be a reference standard for deducing the absolute reflectivities of the Ge-doped core. Then, multi-wavelength refractive index profiles of the Ge-doped core can be further determined based on the Fresnel equation. Moreover, this work shows a connection between the material dispersion of the GRIN rod and the Ge-doped concentrations measured by an energy dispersive spectrometer. Finally, the dependence of the refractive index of the Ge-doped core on the doping concentrations at a certain wavelength can be easily expressed as a linear form.

Exploiting the image of the surface reflectivity to measure refractive index profiling for various optical fibers.

A direct image method of surface reflectivities on a cleaved fiber end with a filtered halogen lamp and a TE-cooled CCD with high dynamic range is proposed to measure the multi-wavelength refractive index profiling (RIP). A polished black glass is used to be a reference standard for measuring the absolute reflectivity of the fiber end. With the developed calibration procedures, both the spatially dependent sensitivity and spectral responsivity of the CCD pixels can be eliminated to achieve the high spatial accuracy. Tested fiber is connected with a fiber terminator to prevent errors from the backside return light. With the present method, the RIP can be precisely measured for not only multi-mode fibers but also single-mode fibers.