The laser-induced damage detection images used in high-power laser facilities have a dark background, few textures with sparse and small-sized damage sites, and slight degradation caused by slight defocus and optical diffraction, which make the image superresolution (SR) reconstruction challenging. We propose a non-blind SR reconstruction method by using an exquisite mixing of high-, intermediate-, and low-frequency information at each stage of pixel reconstruction based on UNet. We simplify the channel attention mechanism and activation function to focus on the useful channels and keep the global information in the features. We pay more attention on the damage area in the loss function of our end-to-end deep neural network. For constructing a high-low resolution image pairs data set, we precisely measure the point spread function (PSF) of a low-resolution imaging system by using a Bernoulli calibration pattern; the influence of different distance and lateral position on PSFs is also considered. A high-resolution camera is used to acquire the ground-truth images, which is used to create a low-resolution image pairs data set by convolving with the measured PSFs. Trained on the data set, our network has achieved better results, which proves the effectiveness of our method.

In-situ laser-induced surface damage inspection plays a key role in protecting the large aperture optics in an inertial confinement fusion (ICF) high-power laser facility. In order to improve the initial damage detection capabilities, an in-situ inspection method based on image super-resolution and adaptive segmentation method is presented. Through transfer learning and integration of various attention mechanisms, the super-resolution reconstruction of darkfield images with less texture information is effectively realized, and, on the basis of image super-resolution, an adaptive image segmentation method is designed, which effectively adapts to the damage detection problems under conditions of uneven illumination and weak signal. An online experiment was carried out by using edge illumination and the telescope optical imaging system, and the validity of the method was proved by the experimental results.

In this study, an improved phase-shifting diffraction interferometer for measuring the surface topography of a microsphere is developed. A common diode-pumped solid state laser is used as the light source to facilitate apparatus realization, and a new polarized optical arrangement is designed to filter the bias light for phase-shifting control. A pinhole diffraction self-calibration method is proposed to eliminate systematic errors introduced by optical elements. The system has an adjustable signal contrast and is suitable for testing the surface with low reflectivity. Finally, a spherical ruby probe of a coordinate measuring machine is used as an example tested by the new phase-shifting diffraction interferometer system and the WYKO scanning white light interferometer for experimental comparison. The measured region presents consistent overall topography features, and the resulting peak-to-valley value of 84.43 nm and RMS value of 18.41 nm are achieved. The average roughness coincides with the manufacturer’s specification value.

We propose an adaptive parallel coordinate (APC) algorithm for quickly forming a series of focused spots at a multimode fiber (MMF) output by controlling the MMF input field with a spatial light modulator (SLM). Only passing over the SLM once, we can obtain SLM reflectance to form focused spots on different positions. Compared with the transmission matrix method, our APC does not require iterations and massive calculations. The APC does not require as much access device time as the adaptive sequential coordinate ascent (SCA) algorithm. The experiment results demonstrate that the time taken to form 100 spots with our APC is 1/54th the time with the SCA.