The Shack-Hartman wavefront sensor is the most widely used real-time wavefront detector in adaptive optics systems. In this study, a Shack-Hartmann sensor with high resolution, high frame rate, and a large-scale sub-aperture number is proposed. Based on the requirements of wavefront processing calculations and real-time performance, a field-programmable gate array (FPGA) is also proposed. The real-time wavefront processor structure and wavefront slope calculation method are investigated. The system employed the core processing module to reuse the method to calculate the centroid of the spot in the sub-aperture and transmitted the processed centroid data to the PC in real time through USB 3.0. The processor was designed with a XILINX Kintex7-325T FPGA processing chip. The results demonstrate that the algorithm can perform low-latency, real-time operations on 1020.1020 images and 56.56 sub-aperture Hartmann sensors at 560 frames per second. The spot centroid calculation increased the wavefront processing speed of the system and the control speed of the entire adaptive optics system.

To conduct research on infrared polarization imaging technology, this paper designs and builds a long-wave infrared polarization imaging system to achieve target time-sharing imaging. An improved differential image correction method based on single-pixel inhomogeneity was proposed to remove cold reflections; the image was registered using the Sobel edge detection method, and the polarization characteristics of the target scene were analyzed. The results demonstrate that the proposed system can obtain the infrared polarization information of the target scene, and the pre-processed image meets the requirements of the experiment. The edge contour and detailed information obtained by the Stokes vector method for the polarization image of the target scene are more abundant. To further measure the performance of the experimental system, the intensity and polarization degree images were fused. Compared with the infrared intensity image, the fused image has a significantly improved image evaluation quality, which verifies the feasibility of the experimental system. This technology can potentially improve target detection efficiency in complex environments in the future.

To meet the requirements of high detection sensitivity, high-precision calculation of spot coordinates, and a calculated output speed greater than 1000 fps, this study introduces a system based on a near-infrared CMOS image sensor to calculate and output centroid coordinates in real time. We employed the NIR detector NOIP1FN1300A of the Python1300 series produced by the company ONsemiconductor to image the laser spot. The Cyclone4 series low-power FPGA was used to drive the sensor and deserialize the sensor output data, and a reliable centroid algorithm was used to calculate the spot coordinates. The results in the output part, USB2.0, and RS422 interfaces were used to output the target image and centroid coordinates in real time. The design has the following advantageous features: 1500 fps real-time processing capacity, a concise software code, accurate calculation of centroid coordinates, and low-power consumption.

In order to increase the angle of field and the detection distance, we designed an infrared imaging seeker using an 640×512 uncooled FPA. Firstly, we analyzed various structural types of infrared imaging seeker. Based on the characteristics of the design requirements, the universal support type structural type is selected. Secondly, we optimized the image quality of the infrared seeker optical system and analyzed the satisfaction of the frame angle and blind area. Finally, the test results of the real prototype show that the infrared imaging seeker has good imaging quality and can meet the requirements of searching and tracking targets.

To study the influence of the asphericity degree on droplet particles optical properties in different gravity fields, the optical properties of water droplet with equal volume and different aspect ratios in the wavelength between 3.0.m and 5.0.m were calculated. It was found that although the changing trend of the optical properties of the water droplets with wavelength is very similar, their specific values significantly depend on the spatial orientation and the asphericity degree of water droplets. In general, the absorption cross section of water droplets strongly depends on its aspect ratio only when the azimuth angle. is small and the wavelength is short. In contrast, the scattering cross section, asymmetry factor, and scattering phase function depend on the aspect ratio of water droplets at any azimuth angle and wavelength. Therefore, because the optical properties are strongly dependent on the aspect ratio of the droplet particles, the radiation transmission properties of fog composed of water droplets should exhibit different results in different gravitational fields.

The key technologies of 3D laser inverted scanning are mainly studied through the introduction of conventional 3D laser positive scanning technology. The inverted scanning incident angle technology was researched, and the hardware tripod inverted installation conditions were determined to attain a reasonable scanning incident angle and achieve a good acquisition effect of point clouds. On the software side, the technology of efficient and automatic noise removal was examined to realize the automatic and efficient removal of noise in inverted scanning. Through experimental comparison, the key technology of 3D laser inversion scanning can be used to achieve better inversion scanning and scanning results. Moreover, it can be used to reduce the intensity of work, improve work efficiency, and expand the application field of 3D laser scanning. The application fields of 3D laser scanning technology have expanded, and can be used as a reference in the application of inverted scanning technology, automatic and high-efficiency noise processing technology, and so on. Furthermore, this method provides a reference for infrared technology in image fusion, image information recognition, image noise reduction, and so on.

Convolutional neural networks are used to solve problems such as complex data preprocessing, low prediction accuracy, and difficulty in dealing with a large amount of nonlinear data in infrared spectroscopy. Moreover, owing to their strong feature extraction ability and good nonlinear expression ability, the application of convolutional neural networks in the modeling of infrared spectrum analysis has attracted attention. In this study, the advantages of the application of a convolutional neural network for the infrared spectrum are analyzed, and the structure and composition of the convolutional neural network are briefly summarized. Then, the dimension problem of the input data in the spectral analysis modeling of the convolutional neural network is described in detail. This paper reviews the influence of convolution kernel parameters in the model design, multi-task processing model, and optimization methods in the training process. Finally, the advantages and disadvantages of this research are analyzed, and future development trends are discussed.

Low SWaP (size, weight, and power) applications are typical features of thermal imaging systems based on HOT(high operating temperature) detectors. The system performance is comparable to that of a cooled infrared system, with reduced manufacturing costs. They have important application value and are promising prospects for high volume production. The structural features of barrier detectors are introduced, and the structures of the materials used for the barrier detectors and their impact on system performance are analyzed. Other technologies used for HOT detectors are also summarized. Finally, the current research progress on barrier infrared detectors is summarized. Additionally, several future research directions for HOT detector technologies are presented.

A Pt/CdS Schottky UV detector was developed and studied based on the engineering application requirements of UV/IR dual-colored detectors. Key technologies such as the chip wafer surface treatment process for CdS, preparation process of the Pt electrode, and annealing of the UV detector chip were studied. The performance of the Pt/CdS Schottky UV detector was also analyzed. The results suggested a photo response rate of more than 0.2 A/W for wavelengths of 0.3–0.5.m and an average transmittance of more than 80% for wavelengths of 3–5.m, which meet the engineering requirements of UV/IR dual-color detectors.

Infrared thermal imaging is a very promising method for evaluating coal and rock damage. To further process the infrared image and extract the key information, the damage status of coal and rock can be distinguished according to this quantitative information. With uniaxial loading, fracture development maps were developed, and synchronous acquisition of infrared images of the rock samples was carried out. The infrared images were analyzed and processed using a normalized histogram, and the details of the infrared images were quantitatively characterized. The results show that the gray value distribution of the infrared images at different times can reflect the surface temperature changes and stress values during the failure process of the sample. When the main fracture occurred, the percentage of pixels in the gray value interval[240,255](the surface temperature of the rock sample was 29.01℃-33.19℃) increased by 13.85% compared with that in the previous moment. In addition, the change trend of the proportion of pixels in the gray value interval[224,255]over time is highly correlated with rock damage variables, which shows that the normalized histogram can characterize the damage and destruction process of the rock mass.

Automatic detection of armored targets has always been the most challenging problem in the field of infrared guidance. Traditional models address this problem by extracting the low-level features of an object and then training the feature classifier. However, because traditional detection algorithms can not cover all object patterns, the detection performance in practical applications is limited. Inspired by the edge-aware model, this study proposes an improved deep network based on edge perception. The network improves the accuracy of the armored contour through an edge-aware fusion module. By exploiting he advantages of the feature extraction module and context aggregation module, it can better adapt to the shape changes of objects and has high detection and recognition accuracy. The results show that the proposed armored detection network model can effectively improve the accuracy of detection and positioning in infrared images.

It has always been technically difficult to compress the high dynamic range data collected by an infrared detector to low dynamic range image data, while preserving the image information as much as possible and improving the contrast of the image. To solve this problem, a new infrared image compression method was proposed. In this method, histogram information is introduced, and the pixels of the background and target regions are distinguished by the segmentation of the histogram. Then, the compression model is established. Finally, enhancing the contrast of the image pixels using different coefficients combines the segmented histogram. The algorithm proposed in this paper uses histogram information to distinguish the pixels of the background region and the pixels of the target region and can effectively suppress background noise when enhancing the image contrast. The experimental results show that the proposed algorithm can better highlight details and improve the contrast.

To address image distortion, texture loss, and low brightness in nighttime fog scenes, this paper proposes a nighttime defogging algorithm based on a dark point light source model. The dark point light source model was first constructed and the degraded image was processed by an algorithm that utilizes both bilateral filtering and limited contrast adaptive histogram equalization. Then, the defogging image was obtained by combining with the atmospheric scattering model. The experimental results show that this algorithm has a fast processing speed, a better effect of nighttime fogging, and a certain degree of improvement in terms of contrast, average gradient, and information entropy when compared with the contrast algorithm. This model can therefore effectively address image distortion, texture loss, and low brightness of fogging images.

When using infrared spectroscopy to analyze the components of natural gas, the obtained spectral signals often contain interference from stray light, noise, baseline drift, and other factors, which affects the resulting quantitative analysis. Therefore, it is necessary to preprocess the original spectrum before modeling. As a potential solution, an SG smoothing method combined with the soft threshold denoising method of the sym6 wavelet function was proposed to preprocess the spectrogram. The traditional preprocessing method and the proposed method are compared and analyzed. The results show that when the proposed method is used to preprocess the spectrogram, the highest goodness of fit value is 0.98652, and the lowest residual sum of squares value is 5.50694, which proves that the function peak fitting effect is the best after using this method, and the processing effect is better than that of the traditional method.