Metal oxides (MOs) have been widely used in photodetection because of advantages such as easy preparation, high stability, and selective transport of carriers. The MO materials exhibit strong light absorption properties. However, there are issues with MO photodetectors such as their low response speed and large dark current owing to the surface effects and defect states. The built-in electric field in the heterojunction can effectively promote the separation of photogenerated electron-hole pairs, thus improving the device response speed and reducing the dark current. Thus, the construction of metal oxide heterojunction photodetectors (HPDs) is of great significance for the further application of MO in the field of optoelectronics. This paper introduces the interface properties of MO and elaborates on the working mechanism of metal oxide HPDs around the PN, PIN, and isotype heterojunctions. Next, the performance parameters of MO/MO and MO/Si HPDs with different structure and response in UV-Vis-NIR band are analyzed and compared. Subsequently, improved methods of the metal oxide HPDs performances are discussed. Finally, the development of metal oxide HPDs is discussed.

Variable F/number-cooled infrared detectors, which are based on variable cold aperture technology, are urgently needed because they can improve the performance of zoom infrared optical systems and consider both spatial resolution and sensitivity. Compared with the traditional zoom infrared optical system, the variable F/number zoom infrared optical system can balance the resolution and sensitivity when the system switches between large and minimum scales. Furthermore, the variable F/number zoom infrared optical system can improve the aperture utilization rate of the optical system and reduce its radial dimensions, which is beneficial for improving the imaging quality and miniaturization design of the infrared optical system. In this paper, we discuss the relationship between the variable F/number and zoom and summarize the research progress in the field of variable cold-aperture infrared detectors. Finally, we analyze the key technical challenges of the mainstream technology approach.

With the application of modern sophisticated weapons and equipment in the military field, target detection and surveillance systems have developed rapidly, which is of great significance for the further study of camouflage materials. This paper discusses the research status of camouflage and stealth textile materials, introduces their application mechanism, focuses on the combination of the most widely used infrared camouflage materials and textile technology, summarizes new research on bionic camouflage textile materials, summarizes new dynamic-change camouflage textiles, and summarizes new detection and preparation technologies for camouflage textiles. Finally, the future development trends of camouflage textile materials are predicted and analyzed.

Scanning microlens array systems can effectively resolve the contradiction between small strokes and large fields of view using micromotion scanning imaging. They generally adopt the Keplerian telescope structure and perform field-of-view scanning through the relative lateral displacement of the lenses. In this paper, we propose a four-piece microlens array based on the Keplerian telescope structure and evaluate the effect of the angular magnification of the microlens array on the scanning microlens array system in the 3-5 μm band. When the angular magnification is less than 1, more stray light is generated after crosstalk and the upper limit of the energy utilization of the system is limited, resulting in a restricted diffraction limit. Higher angular magnifications increase the upper limit of energy utilization. When the angular magnification is changed from 0.67^× to 0.83^×, the energy utilization increases from 43% to 69%. When the angular magnification is greater than 1, the energy utilization of the system is no longer limited by the structure, and the structure with an angular magnification of 1.5^× is optimized under the condition of suppressing crosstalk. The results of the image quality evaluation are as follows: the RMS radius of each scanning field reaches the pixel size of the detector, and the MTF reaches 0.6@17l p/mm. As a parameter characterizing the structure of the microlens array, the angular magnification is related to the energy utilization of the system, which affects the image quality. Therefore, the analysis and study of angular magnification can provide a basis for the design and implementation of the scanning microlens array system.

High-precision integrated star sensors have a high pointing accuracy and are highly sensitive to temperature changes. The external heat flow in near-Earth orbits is complex and variable. The comprehensive factors of the integrated structure and the concentration of the internal heat source not only lead to difficulty in the heat dissipation design, but also make it difficult to guarantee the pointing accuracy of the lens directly affected by the internal heat source. First, combined with the orbital parameters, the installation layout provides the average absorbed external heat flux of the star sensor. Subsequently, by analyzing the working conditions of the external and internal heat flows, a thermal design method combining passive and active thermal control is adopted, and the position and size of the heat dissipation surface of the star sensor are designed and calculated. Finally, thermal analysis and verification are performed using thermal simulation software according to the orbital environment and thermal control measures. The simulation results show that the installation flange temperature is 19.82-20.10℃, the axial temperature difference of the lens is less than 2.23℃, the circumferential temperature difference is less than 0.48℃, and the circuit box temperature is 19.10-23.49℃, which meets the thermal control index. The stable working conditions of the extremely high-precision star sensor are ensured by a reasonable thermal control design, and the thermal design of the star sensor is reasonable and effective.

To avoid obscuring the field of view, a photoelectric platform is usually placed on top of a lifting mast. The structural stiffness of the mast directly affects the stiffness of the photoelectric platform servo system and its dynamic performance. In actual use, it is deduced that an insufficient stiffness of the mast causes the photoelectric platform to resonate easily in the process of azimuthal rotation, causing it to work unstably. To solve this problem, a Butterworth low-pass filter, notch filter, and linear auto disturbance rejection control mode are introduced successively to suppress the resonance phenomenon of the lifting photoelectric pan tilt table. By comparing the advantages and disadvantages of the three methods, it is concluded that the linear ADRC method has the advantages of fast response, good resonance suppression effect, and good robustness, and is suitable for the control system of the lifting photoelectric photoelectric platform.

To evaluate the nature of the low gloss properties of Al-Sr10, X-ray diffraction, X-ray energy dispersive spectroscopy, face scan elemental analysis, 8−14 μm infrared emissivity, gloss, and 400−760 nm visible reflectance tests are performed on the material. The Al-Sr10 filler is prepared by high-energy ball milling, and changes in the components, morphology, and optical properties of the Al-Sr10 filler with the ball milling time are evaluated by scanning electron microscopy. The results show that the surface of Al-Sr10 is easily oxidized to form gray-black strontium oxide, and the effect of strontium in the solid solution leads to the extinction of the material. The components of the Al-Sr10 filler do not change with the ball milling time, and the lamellarization, emissivity, and gloss properties of the material increase with time. The degree of lamellarization of the filler after 15 h of ball milling is high, the emissivity is as low as 0.123, and the gloss is as low as 3.8.

In heterogeneous image registration, because of the differences in the imaging mechanisms, image pixel intensity correlation and rotation distortion are two inevitable problems. Aiming at the problem of image pixel intensity correlation, an image registration algorithm based on a radiation-invariant feature transform (RIFT) is proposed; it has good accuracy for image registration with small differences in the pixel correlation between images, but produces more error matching for rotation distortion images. For the problem of rotational distortion, the traditional Oriented Fast and Rotated Brief (ORB) algorithm has a certain degree of stability in the registration of rotating images; however, for image pairs with insignificant intensity changes, the quality of the feature point detection is low and the registration accuracy is not ideal. Therefore, this study integrates Phase Consistency into the ORB algorithm, replaces traditional image strength information with phase information, and constructs a rotation-invariant BRIEF feature descriptor that is robust to changes in the pixel strength and rotation distortion in the image. The registration experiment is conducted using infrared and visible-light images with unclear pixel intensity correlations. The algorithm proposed in this paper has high registration accuracy for images with different rotation amplitudes, and the RMSE is stable at 1.7−2.1, which is superior to the RIFT algorithm. It performs well in detecting a large number of feature points, achieving high registration accuracy, and maintaining efficiency.

Current infrared image super-resolution reconstruction methods, which are primarily designed based on experimental data, often fail in complex degradation scenarios encountered in real-world environments. To address this challenge, this paper presents a novel deep learning-based approach tailored for the super-resolution reconstruction of infrared images in real scenarios. The significant contributions of this research include the development of a model that simulates infrared image degradation in real-life settings and a network structure that integrates channel attention with dense connections. This structure enhances feature extraction and image reconstruction capabilities, effectively increasing the spatial resolution of low-resolution infrared images in realistic scenarios. The effectiveness and superiority of the proposed approach for processing infrared images in real-world contexts are demonstrated through a series of ablation studies and comparative experiments with existing super-resolution methods. The experimental results indicate that this method produces sharper edges and effectively eliminates noise and blur, thereby significantly improving the visual quality of the images.

Infrared imaging of SF6 gas is easily affected by environmental noise and exhibits low contrast and signal-to-noise ratio. As a result, existing algorithms cannot adaptively enhance the SF6 leakage area or suppress Gaussian noise. Therefore, this study proposes an improved HE-based SF6 leakage-area enhancement algorithm. The algorithm first uses SSR to process the original SF6 image to obtain the reflection image R, and then uses guided filtering to decompose the reflection image R into detail and base layers. Finally, an improved histogram equalization is used to adaptively process the base layer, and the enhanced images are fused to obtain the final image. The experiment results demonstrate that the proposed algorithm can not only adaptively enhance the contrast of the leaked area but also has good edge preservation and Gaussian noise suppression performance. Its enhancement effect is superior to that of the existing SF6 infrared image enhancement algorithm. This effectively improves the low-contrast and low signal-to-noise ratio characteristics of the SF6 infrared images.

To address the shortcomings of infrared and visible light object detection methods, a detection method based on an adaptive attention mechanism that combines deep learning technology with multi-source object detection is proposed. First, a dual-source feature extraction structure is constructed based on deep separable convolution to extract the features of infrared and visible objects. Second, an adaptive attention mechanism is designed to fully complement the multimodal information of the object, and the infrared and visible features are weighted and fused using a data-driven method to ensure the full fusion of features and reduce noise interference. Finally, for multiscale object detection, the adaptive attention mechanism is combined with multiscale parameters to extract and fuse the global and local features of the object to improve the scale invariance. Experiments show that the proposed method can accurately and efficiently achieve target recognition and localization in complex scenarios compared to similar object detection algorithms. Moreover, in actual substation equipment detection, this method also demonstrates higher generalization and robustness, which can effectively assist robots in completing object detection tasks.

A Sobel gradient histogram equalization(GHE) algorithm is proposed to enhance the contrast of infrared images during dynamic range compression. In contrast to previous histogram equalization(HE) methods, this method adaptively assigns a high contrast to the strongly graded parts of the image, preserving and enhancing more details in the 16-bit image. Dual Gamma mapping is then used to adjust the mapping curve to effectively suppress overexposure in the bright parts of the image while improving the detail in the shadows. Compared with the traditional histogram equalization algorithm, this method has better effects on dark area detail processing, overexposure suppression, and contrast enhancement.

Microchannel plates (MCP) are the core components of super GEN Ⅱ and GEN Ⅲ low-light-level image intensifiers. The spatial resolution has a significant effect on the resolution, transmission, and halo performance of low-light-level image intensifiers. The research results at home and abroad are reviewed based on the new technology development of the MCP used by the most advanced super GEN Ⅱ and GEN Ⅲ intensifiers. The specific performance requirements of advanced image intensifiers for the high spatial resolution of microchannel plates are verified by a systematic analysis of the three stages of photon input to the MCP surface, MCP electron multiplication, and multiplication electron image output in the imaging process of the image intensifier. The development trends of domestic MCP is put forward: the MCP with an aperture of 5 μm, an opening area ratio of approximately 70% and optimized output electrode will be developed and applied in batch in the next few years. The MCP applied to the super GEN Ⅱ image intensifier needs to conduct research on new technologies such as a small aperture funnel MCP technology and electron deceleration film, so that the MCP opening area ratio can reach more than 90% and the modulation transfer function and contrast performance of the image intensifier can be significantly improved. MCP with a low outgassing and low ion feedback are needed in the research to support the unfilmed GEN Ⅲ, which can inhibit Halo and improve the signal-to-noise ratio. Based on the unfilmed intensifier, the funnel MCP technology, input enhancement film technology, and electron deceleration film technology have the potential to be used in GEN Ⅲ intensifier.

The micro miniature refrigerator (MMR) is a novel Joule-Thomson cryocooler manufactured using micromachining technology, and its axial length is significantly shorter than that of traditional Joule-Thomson cryocoolers used in infrared detectors. MMRs can significantly reduce the size of infrared detectors when they are successfully integrated. To study the working mechanism of MMRs, a microchannel flow calculation model is established considering the high working pressure and significant change in the gas properties along the microchannels, and the calculation model is verified experimentally. The heat transfer characteristics, microchannel distribution, and overall dimensions of the MMRs are further investigated. Furthermore, an MMR prototype is fabricated based on the calculation results and its cooling performance is studied experimentally. The experimental results correspond well to the predictions of the calculation model. The MMR prototype achieved cooling temperatures of 110 K and 119 K under 10 MPa N₂ and Ar working conditions, the cooling power reaches 231 mW and 479 mW, and the cool-down times are 250 s and 70 s, respectively. Consequently, the cooling performance of the MMR prototype is superior to that of the foreign MMR and meets the cooling requirements of infrared detectors.

An image processing method based on the HSV space model with an improved K-means clustering algorithm is proposed to accurately identify and extract the hot spot part of photovoltaic modules. First, the infrared image is transformed into the HSV space and bilaterally filtered to remove noise and improve the image contrast. Second, the Gaussian kernel function is used to extract the image grayscale probability density function, and then the initial clustering center is obtained. Finally, K-means clustering is applied to the image using prior knowledge to extract and quantify the hot spot defects. The research results show that the method can quickly detect and locate the hotspot position and calculate the degree of damage to the photovoltaic panel, and has high accuracy, good sensitivity, and stability.

Carbon fiber reinforced composites are widely used in aviation field, which requires higher quality. But the traditional manual detection method has high work intensity and low efficiency. In order to improve the defect detection efficiency of carbon fiber reinforced plastics (CFRP), this study designed a defect detection system based on LabVIEW software development platform, extracted defect edges and performed quantitative statistics. In this study, active infrared thermal imaging non-destructive testing technology is used to obtain the surface thermal images of damaged samples scanned by laser through infrared thermal imager. In view of the poor contrast and uniformity of infrared images, HSL(Hue, Saturation, Luminance) is used to carry out color plane extraction and gray transform, and Niback local threshold segmentation algorithm suitable for processing images with uneven illumination distribution is selected to carry out threshold segmentation processing of image of the region of interest. Finally, morphological processing is used to enhance the image and realize defect feature extraction and defect number statistics. In this study, an infrared thermal imaging defect detection experiment platform is built to complete the acquisition and processing of infrared thermal wave defect images, and the software platform and user interface are designed to realize the extraction of the defect features. Compared with manual detection, the design of this system significantly reduces the detection time and is helpful in realizing the automation of defect detection.