The HgCdTe multilayer heterojunction technology is an important direction for the development of mainstream infrared detectors in the future, playing an important role in high-performance infrared detectors, such as high operating temperature (HOT) detectors, dual/multicolor detectors, and avalanche photodiodes (APDs). Recently, HgCdTe HOT infrared detectors based on multilayer heterojunction technology have been developed, particularly devices based on the barrier and non-equilibrium operating P+-π(ν)-N+ structure have been widely studied. In this review, the dark current suppression mechanisms of P+-π(ν)-N+ structure HgCdTe infrared detectors with barrier and non-equilibrium operations were systematically introduced, the key problems that restrict the development of these two types of devices were analyzed, and the relevant research progress was reviewed. We summarized and assessed the prospects of the development of multilayer heterojunction HgCdTe infrared detectors.

To meet the demand for third-generation infrared detectors in multi-band detection, a mid-/mid-wavelength dual-color detector can obtain target information in two bands simultaneously and suppress complex background; hence, it can effectively eliminate the influence of interference sources and improve the accuracy of detection, which enhances target recognition under artificial and complex background interference. The design and preparation of mid-/mid-wavelength dual-color detectors have recently developed rapidly. The InSb infrared detector can realize the detection of the mid-/mid-wavelength via light splitting, and the antimonide type-II superlattice detector realizes multi-band detection through the energy band structure design. This paper describes the main technical method and current research progress of antimonide mid-/mid-wavelength dual-color infrared detectors. Compared with traditional InSb dual-color detectors, mid-/mid-wavelength dual-color superlattice infrared devices have distinct characteristics and advantages for infrared imaging detection. However, further research on detector structure design, antimonide superlattice material growth, and array device preparation is required to improve the detection performance and meet the demands of engineering applications.

A target detection method based on infrared and visible image fusion is proposed to overcome the shortcomings of the existing target detection algorithms based on visible light. In this method, depth separable convolution and the residual structure are combined to construct a parallel high-efficiency feature extraction network to extract the object information of infrared and visible images, respectively. Simultaneously, the adaptive feature fusion module is introduced to fuse the features of the corresponding scales of the two branches through autonomous learning such that the two types of image information are complementary. Finally, the deep and shallow features are fused layer by layer using the feature pyramid structure to improve the detection accuracy of different scale targets. Experimental results show that the proposed network can completely integrate the effective information in infrared and optical images and realize target recognition and location on the premise of ensuring accuracy and efficiency. Moreover, in the actual substation equipment detection scene, the network shows good robustness and generalization ability and can efficiently complete the detection task.

The fusion of panchromatic images (Pan) and multi-spectral images (MS) is designed to generate multi-spectral images with high spatial resolution. A fusion method based on detailed information extraction is proposed to improve the quality of the fused images. First, the high-frequency components of Pan and MS are obtained by a rolling guidance filter and margin calculation, respectively. Second, the adaptive intensity-hue-saturation (AIHS) transform is used to process the high-frequency components of MS and Pan, determined by the pixel significance, to generate the corresponding intensity component (I, intensity). Then, the difference between Pan and I is calculated to obtain the detailed image. Then, the residual image is obtained by calculating the difference between the high-frequency components of Pan and MS with a guided filter. Finally, the detailed and residual images are integrated with the original MS image using the steepest descent method to obtain the final fusion result. The experimental results demonstrate that the fused images obtained by the proposed algorithm can achieve better subjective visual effect. Simultaneously, the objective evaluation indicators are better.

Military equipment, such as infrared warning systems and infrared image-guided missiles, requires a number of infrared simulation images during performance evaluation and simulation training. However, the infrared simulation image generated by infrared simulation software has some problems, such as poor fidelity and poor universality. The domestic infrared simulation technology is hampered by foreign technology blockade. Currently, the development situation is difficult in meeting the requirement of the application. To solve this problem, this experiment proposed a visible-infrared image transform method. First, the region seeds growing (RSG) algorithm was used to extract the water target from the collected image. Subsequently, the visible and infrared image water target datasets were established. Finally, the trained cycle generative adversarial network (CycleGAN) was used to generate the infrared simulation image from the visible image. The test results show that the visual effect of the infrared simulation image generated by this method is close to that of the real infrared image and can be applied to the naval infrared military equipment simulation test and training system.

A registration fusion method of infrared and visible images of power equipment based on deep learning is proposed that aims at problems with difficult and long registration time of infrared and visible images of existing power equipment. In this study, feature extraction and feature matching are combined in a deep learning framework to directly learn the mapping relationship between image block pairs and matching labels for subsequent registration. In addition, a self-learning method using infrared image and its transform image to learn the mapping function is proposed to alleviate the problem of insufficient infrared image samples during training Simultaneously, transfer learning is used to reduce the training time and accelerate the network framework. The experimental results show that the performance index of this method is significantly improved compared with the other four registration algorithms. The average accuracy of this method is 89.909, which is 2.31%, 3.36%, 2.67%, and 0.82% higher than that of the other four algorithms, respectively. The average RMSE of this method is 2.521. Compared with the other four registration algorithms, the algorithm is reduced by 14.68%, 15.24%, 4.90%, and 1.04%, respectively. The average time of the algorithm is 5.625 s, which is reduced by 5.57%, 6.82%, 2.45%, and 1.75% respectively. The efficiency of infrared and visible image registration of the power equipment must be effectively improved.

A modified bi-histogram equalization algorithm is proposed to suppress gray saturation and loss of local details caused by global histogram equalization. First, the background and foreground of the image are segmented, and a modified k-means clustering algorithm based on the local minimum of the histogram is proposed to determine the ideal segmentation threshold of the image. Then, histogram equalization is performed for the segmented sub-graphs. The algorithm is verified by experiments; the results for the experiment show that, compared with those from global histogram equalization, the peak signal to noise ratio and structural similarity are improved by approximately 16.425% and 14.85%, respectively. Simultaneously, through subjective evaluation, the algorithm based on histogram local minimum and modified k-means can effectively suppress the gray saturation and detail loss caused by GHE.

Auto-gating power supply is the energy source of low light level image intensifiers, and research on its local bright-light protection method is of great significance for improving the control strategy of auto-gating power supply and the bright-light performance of low light level image intensifiers. This paper analyzes the advantages and disadvantages of the low light level image intensifier that matches the DC high-voltage power supply and auto-gating power supply in the application of a large dynamic illumination range. A design idea for an auto-gating power supply is presented, which can provide the low light level image intensifier with high illumination application and low illumination local bright-light protection and offers a control strategy for an auto-gating power supply circuit.

The continuous zoom function is one of the important features of the current advanced thermal imaging cameras, and the zoom cam is the key component to drive the movement of each lens group in the continuous zoom optical system. First, this study applies the dynamic optics theory to deduce the image movement compensation group formula of the zoom optical system to obtain the trajectory curve of the image movement compensation group and design a zoom cam structure with good performance. Then, we use the sequential quadratic programming (SQP) optimization algorithm. Combined with the optomechanical design theory, CREO is used to generate the cam curve and cut the cam groove to obtain the zoom cam structure and reduce the pressure angle of the dynamic optical curve. Then, the cam structure is analyzed based on the finite element analysis theory. Finally, the motion of the zoom system and the imaging results confirm the feasibility of the proposed method.

In infrared imaging systems, in which the core is an infrared focal plane array, a microscanner can enhance the spatial resolution of the entire system. To test microscanners, this study developed a measurement and calibration method based on image processing and built a system to measure and calibrate microscanners. Using a microscanner as a test subject, the test results indicate that the proposed method has a significant effect on the measurement accuracy, repetition accuracy, and uncertainty. The method can provide technical support for the design and manufacture of microscanners.

The partial discharge defect characteristics of cable terminals are short, and the defect range is entangled with the external environment, making it difficult to accurately locate. It must be detected along with the temperature characteristics and pattern recognition characteristics. In this paper, using the advantages of ultrasonic infrared thermal imaging, a partial discharge defect detection method for cable terminals based on ultrasonic infrared thermal images is proposed. This method uses image gradient grayscale to collect an ultrasonic infrared thermal image of the partial discharge defect characteristics of a cable terminal, suppress the complex background of the collected image via an intelligent pattern recognition processing method, and delete large-area ground objects and surfaces contained in the image. Using the K-means clustering algorithm, the characteristic range of the suspected partial discharge defects is delineated, and the partial discharge defect range template is constructed. After matching the reference range, information on the temperature characteristics of the suspected partial discharge defect range is obtained to diagnose whether there are partial discharge defects in the cable terminal. The experimental results show that this method can effectively obtain the partial discharge defects of cable terminals. The average accuracy of detecting different types of partial discharge defects in cable terminals was as high as 98%, and the average missed detection rate was 1%.

Pseudo soldering with a normal appearance and electrical connections cannot be effectively detected by the existing detection technology. Pulse infrared detection technology was applied, and the trend that analyzes processes was adopted to establish the relationship between the degree of pseudo soldering and characteristic parameters of the infrared fitting curve. It is difficult to distinguish the background noise, and the signal of the pseudo soldering from excess temperature was successfully resolved. Results show that the resolution and signal-to-noise ratio were improved.

According to the application prospects of micro drone radar, precision guided weapon radar, and wireless communication terminal equipment, an asymmetric primary and secondary waveguide directional coupler is designed. The coupler uses an equally spaced porous coupling structure with different shapes of main and secondary waveguides, and the signal of the TE10 mode of the rectangular waveguide is coupled to the TE11 mode of the secondary circular waveguide. The isolated port achieves the effect of reverse cancelation, and good coupling and isolation can be obtained using the principle of phase superposition. The center frequency of the directional coupler and the relative bandwidth are 400 and 40 GHz, respectively. The results show that the coupling degree of the directional coupler is approximately -13.8 to -12.8 dB, which achieves a weak coupling effect and has a good coupling stability. The isolation is better than -24.5 dB, the through insertion loss is approximately -3 to -2.5 dB, and the performance is good.

Effective and high-precision non-destructive detection methods for building diseases are lacking, and traditional manual detection imposes limitations on the height and area of the disease area of the building envelope facing layer. To address these issues, this study proposes a method of 3D fusion of infrared imaging and oblique photography along with the establishment of 3D model electronic archives containing building disease information. Taking a teaching building in a school as an example, this study adopts the acquisition scheme mainly based on infrared technology and supplemented by tilt photography technology and the coordinate matching method in the same position space, obtains the refined three-dimensional model electronic file containing building disease information through coordinate conversion and two kinds of heterogeneous spatial data fusion, and completes the comparative evaluation of model accuracy before and after data fusion. The results show that the fusion model obtained by this method has a high accuracy and small point error and can quickly and accurately obtain the spatial position of building diseases. The method provides a new idea for the practical application of building nondestructive testing technology and has research value and practical application significance for establishing an integrated operation and maintenance system of building information supervision, repair, and management.(YCSW2020164)。