To solve the problems of weak signal strength and low signal-to-noise ratio in traditional Raman spectroscopy, a new confocal Raman system is proposed in this paper. The absolute conjugation of the confocal point is realized by external photonic crystal fiber. The technical problems in the coupling process of photonic crystal fiber are summarized, and the actual samples are tested. Compared with conventional confocal Raman fibers such as Thorlabs and OZ and Witec 532 nm-alpha300R Raman system, the signal-to-noise ratio in this paper is 73.8382 at the same laser intensity and integration time, which is significantly higher than that of Thorlabs and OZ (37.1557 and 40.0342, respectively). Compared with the signal-to-noise ratio of 65.5312 for Witec 532 nm-alpha300R, it also increased by 12.68%. High-quality Raman signal makes the absolute conjugated confocal Raman system have broad market prospects and ultra-high market competitiveness.

With the development of full-screen mobile phones, the need for under-screen imaging of mobile phones has emerged. However, the diffraction caused by the wiring and other opaque parts will affect the image quality of the under-screen image. In this article, under-screen image is restored from the perspective of image restoration. The point spread function (PSF) of the mobile phone imaging system is obtained through actual measurement, and the image is deconvolved using the measured PSF. In this article, traditional deconvolution method has been improved, in which the color space of the image is converted and different channels are processed separately. Compared with the traditional deconvolution method, the results of the sub-channel deconvolution method have improved structural similarity (SSIM), peak signal-to-noise ratio (PSNR) and other indicators, and the required running time is shorter. After sub-channel deconvolution, the non-local averaging algorithm is used for denoising, which further improves the quality of the under-screen image.

The liquid crystal optical phased array (LCOPA) is the core device of next-generation beam control technology. Improving its laser-induced damage threshold is one of the current research hot spots. Aiming at the scene of higher power laser incidence, the degradation degree of LCOPA phase modulation performance should be evaluated. Based on the traditional quarter-wave plate method, this paper realizes fast and direct measurement of the phase modulation of the liquid crystal to the incident laser. The verification test found that when the core temperature is 33 ℃, the corresponding maximum phase aberration is 3.6 rad. At the same time, based on the measured phase modulation results, this paper studies the deterioration process of the beam quality of the outgoing light. Analysis results show that the deterioration of beam quality is less than 20% when the core temperature of the liquid crystal phase shifter changes less than 10 ℃.

To solve the problem of motion blur in abnormal behavior detection, a fast motion blur removal algorithm, based on DeblurGAN, is proposed. Three 3×3 convolutions are used to replace the 7×7 convolution in the original generator. The transposed convolution is discarded. Firstly, bilinear interpolation is used to expand the size of the feature map which needs upsampling. The residual unit is replaced by a residual density block (RRDB) in the original algorithm. The RRDB is then scaled to 0~1 to avoid unstable training. The L1 loss of gradient images is added to the loss function of the original generator. As the DeblurGAN reconstructed image edge is often not clear enough, the edge information of the image is added to make the reconstructed image edge more obvious. The effectiveness of this method is verified by experiments and is compared with other similar algorithms like DeblurGAN. The PSNR of the optimized model is improved by 0.94. The structure similarity and speed are equivalent. The chessboard lattice problem in the reconstructed image is solved. The edge of detail is more prominent. The performance of the proposed model is better than that of other related algorithms.

In order to respond to the scintillator screening requirements of large scientific projects and the new medical imaging equipment such as the development of large-scale collider experimental detectors, space load calorimeters and TOF-PET, our laboratory conducts research on the scintillation performance (emission spectrum, light output, energy resolution, decay time, afterglow, coincidence time resolution, etc.) of scintillators. A complete set of inorganic scintillator performance test programs is designed for the optimal performance of different scintillator samples. In the test of emission spectrum, different excitation sources were selected for comparison test. The energy resolution and the test conditions of the scintillation performance such as time resolution were optimized, which were successfully applied to the performance research of popular scintillators including cerium-doped yttrium lutetium silicate (LYSO:Ce) and gadolinium aluminum gallium garnet (GAGG:Ce), and good test results were obtained. The energy resolution of LYSO:Ce and ceramic GAGG:Ce scintillators are 7.9% and 5.4%, respectively, and the coincidence time resolution of the LYSO:Ce scintillator can reach 94.3 ps.

In order to realize the miniaturization and integration design of space optical communication system, an integrated tracking system based on the array detector and the fast deflection mirror is established. By analyzing the principle of spot position detection of array detector, a centering algorithm is proposed. Firstly, the coarse centering strategy is designed by setting the threshold value. Then, the fine centering is completed by using the database query method. Thirdly, the infinite integral method is used to make the spot return to the origin. Finally, the correctness and feasibility of the algorithm are verified by building an experimental platform. The experimental results show that the tracking field of view can reach 70.3 mrad, which is about 3 times larger than that of the original algorithm, and the maximum tracking error is better than 1.8 μrad, which lays a foundation for further engineering application of the space optical communication system.

Blind image restoration aims to accurately estimate the blur kernel and the wanted clear image with no-reference. Existing researches show that the use of the Total Variation to model the high-order image gradient prior constraints can effectively suppress the blocking artifact generated in the restored image. On the basis of experimental observation and research, this paper proposes to use the sparse prior constraint model to regularize the blind restoration process to obtain a better image restoration performance. Our method makes use of the sparsity of the high-order gradient of the image and combines it with the low-order gradient to construct the mixed gradient regularization term. At the same time, an adaptive factor based on image entropy is introduced to adjust the ratio of the two types of gradient priors in the iterative optimization process so as to obtain better convergence. Simulated and experimental results prove that compared with the existing state-of-the-art methods of blind image restoration, the proposed method has superior image restoration performance.

The coal port will produce dust in the process of unloading coal by the chute of the ship loader. In order to solve the problem of dust detection, this paper proposes a method of coal dust detection based on deep learning (YOLOv4-tiny). The improved YOLOv4-tiny network is used to train and test the dust data set of chute discharge. Because the detection algorithm cannot get the dust concentration, this paper divides the dust into four categories for detection, and finally counts the area of detection frames of the four categories of dust. After that, the dust concentration is approximately judged through the weighted sum calculation of these data. The experimental results show that the detection accuracy (AP) of four types of dust is 93.98%, 93.57%, 80.03% and 57.43%, the average detection accuracy (mAP) is 81.27% (which is close to 83.38% of YOLOv4), and the detection speed (FPS) is 25.1 (which is higher than 13.4 of YOLOv4). The algorithm can balance the speed and accuracy of dust detection, and can be used for real-time dust detection to improve the efficiency of suppressing coal dust generated by chute discharge.

During the preparation and use of the optical thin film, the absorption center will be generated due to defects and pollution. When the optical thin film is irradiated by a laser, the absorption center absorbs light energy and generates thermal signals, according to which the optical absorption loss of an optical film can be measured. The method proposed in this paper for measuring the optical absorption loss of a thin film based on a thermal imager. The addition of a reference sample in the test can reduce the impact of the changes of environmental temperature and the thermal imager noise on the temperature test results. Taking a certain area of the temperature field recorded by the thermal imager during the entire laser irradiation process can reduce the errors of the finite element simulation calculation caused by the laser pointing fluctuations and the unsatisfactory spot distribution. Using this method, the absorption loss of a small-size 45° high-reflection film was tested to be 7.60 ppm, and the spatial distribution of the absorption loss of the same batch of large-size optical film samples were tested. The absorption of the optical film measured by this method is consistent with the result of the laser calorimetry test. This method does not require long-term constant temperature and strict environmental temperature control, and the tested sample size is not limited.