Few studies have presented mathematical models and test methods for determining the radiation gain of AlGaN photocathode solar blind ultraviolet image intensifiers; thus, in this study, a mathematical model of radiation gain of a dual close-focused AlGaN photocathode ultraviolet image intensifier, with an effective diameter of .18 mm, is deduced. The mathematical model is based on the brightness gain of low-light-level image intensifiers; two values of incident irradiance are introduced to the photocathode and a visual function to the phosphor screen. The quantum efficiency of the photocathode, MCP(microchannel plate) current gain, and luminous efficiency of the phosphor screen are measured using standard laboratory equipment. The measured values of the parameters are substituted into the mathematical equation of the radiation gain, and the theoretical values of radiation gains of 10 samples are calculated. Additionally, a set of UV radiation gain test systems is modified; the systems are used to test the radiant gain of the aforementioned 10 samples and compare the mathematical theory and the actual measured values. The deviation between the two values is within 10%, and the validity of the mathematical model and test system are proven. The results of this study may serve as a reference for studies regarding high-radiation gain UV image intensifier technology.

A novel model is proposed for the object detection of solar-blind ultraviolet corona using the Poisson distribution and based on the photoelectric detection theory. This model can be used to represent the spatiotemporal characterization of solar-blind ultraviolet corona. In ultraviolet image sequences, initially, the existence of ultraviolet corona is determined by the statistical model of dark noise. Subsequently, the Poisson distribution map of the ultraviolet image is obtained using the proposed model. Finally, an improved Otsu’s method is applied to detect and extract the object of ultraviolet corona. The proposed method is successfully tested over many ultraviolet image sequences and compared with the other two methods. The experiment re-sults demonstrate that the proposed method performs better in object detection and has higher accuracy in ultraviolet image sequences.

A “solar blind” ultraviolet photomultiplier is a key detector for ultraviolet warning systems andultraviolet communications. It exhibits the characteristics of high radiation sensitivity, high gain, highresolution, and low noise. Therefore, ultraviolet photomultipliers are an important research topic undervacuum devices worldwide. Herein, a “solar blind” ultraviolet photomultiplier developed by NVT isintroduced from four aspects: structure design, cathode preparation, microchannel plate, and whole tubesealing. A .18 mm Cs2Te photocathode was evaporated on an MgF2 window through anti-reflectiontechnology, which resulted in a radiation sensitivity at 250 nm increasing from 22 to 26.5 mA/W. The signalrise time is less than 500 ps, and the TTS is less than 0.1 ns; these are obtained by electron optics simulation.The gain of the microchannel plate reached 5.106 via structure optimization and atomic layer depositiontechnology. The indium sealing technology of glass/Cr/Cu/Ag multilayer metal film is adopted to improvethe qualified rate of the entire tube sealing to 97%.

The limiting resolution of an AlGaN photocathode image intensifier tube is much less than the limiting resolution of a GaAs photocathode image intensifier tube; thus, based on the effect of ultraviolet fluorescence, we calculate the limit resolution of an AlGaN photocathode and promote its ultraviolet modulation transfer function. According to the proximity focus system modulation transfer function and the test result of the custom developed AlGaN photocathode image intensifier, the resolution model of the AlGaN photocathode image intensifier is established. By comparing the ultraviolet modulation transfer function with the proximity focus system modulation transfer function, we discovered that the ultraviolet and photoelectron transmission in an AlGaN photocathode can reduce the limiting resolution of the AlGaN photocathode image intensifier tube and that the main reason affecting the limiting resolution of the AlGaN photocathode image intensifier tube is the photoelectron transmission and the scatter in the AlGaN photocathode.

A microchannel plate(MCP)is a superfast response electron multiplier that is widely used in image intensifiers and photomultipliers. Herein, first, the principle of MCP input enhancement film is introduced; subsequently, a film with a high secondary electron emission coefficient is deposited on the input end of the MCP via vacuum coating, and the characteristics of the film are characterized by surface resistance and X-ray photoelectron spectroscopy(XPS). Through experiments, the signal-to-noise ratio, MCP gain, and image intensifier resolution of the coated MCP and conventional MCP image intensifier are measured. The results show that the signal-to-noise ratio of the image intensifier and the MCP gain of the coated MCP are higher than those of the conventional MCP; however, the resolution of the image intensifier is lower. For the conventional and coated MCP image intensifiers, the signal-to-noise ratios are 25.27 and 29.53, respectively; the average MCP gains are 209.5 and 450.5, respectively; the average resolutions of the image intensifier are 61 and 54.75 lp/mm, respectively. The increase in the signal-to-noise ratio of the image intensifier and MCP gain are ddue to the inncrease in thee surface secondary electrron emissionn coefficient aafter the MCCP input is coated. Inn addition, owwing to the iincrease in thhe secondary electron emiission coefficcient of the MMCP input surface, tthe number oof scattered electrons onn the MCP iinput surfacee increases, tthereby decreeasing the resolutionn of the MCP image intenssifier.

Using the electronic data acquisition system, various performance tests of microchannel plate type photomultiplier tubes (MCP-PMT) at different focus levels were performed in the geomagnetic environment, such as single photoelectron spectrum and after pulse, transit time fluctuations, signal rise and fall time, collection efficiency and so on. By changing the relative position of the photomultiplier tube and the geomagnetic field, the performance of the MCP-PMT at different angles was measured, and the geomagnetic field was qualitatively analyzed by comparing the performance of MCP-PMT under the shielding magnetic field and geomagnetic field.

To investigate the common C pollution phenomenon of the microchannel plate (MCP) with an ion barrier film, we conducted vacuum heating, ultraviolet irradiation, and hydrogen heating tests. Auger electron spectroscopy and MCP electrical performance test are performed on each test result. Furthermore, the trends of MCP C content and MCP performance are analyzed using different methods. The analysis results show that the reduction in C content significantly improved MCP performance. The vacuum heating process does not significantly affect the decomposition of C; however, the UV irradiation and hydrogen heating processes can completely remove MCP C pollution. Finally, hydrogen heating significantly improved the MCP gain performance and resulted in the highest efficiency.

In this study, the effect of lye corrosion time on the gain of a microchannel plate is investigated. By adjusting the lye-etching time, the concentration of alkali metals and the roughness of the channel inner wall are visualized via X-ray photoelectron spectroscopy and atomic force microscopy. Results show that reducing the lye-etching time can reduce the loss of alkali metal elements in the inner wall of the microchannel plate without affecting its roughness, thereby improving the secondary electron emission capacity and the microchannel plate gain. Further tests show that higher alkali metals can improve the gain of both the microchannel plates and image intensifier tubes, whereas the noise coefficient in the image intensifier tube does not increase with reduced lye-etching time.

To improve the resistance of long wave infrared (LWIR) band antireflective films on Ge in harsh environments, film design and fabrication technology have been discussed using LaF3 as a low refractive index material. We fabricated high-performance multilayer antireflection (AR) coatings in an LWIR band (8–12 .m) via an optimized film structure and sectional preparation of LaF3 layer on Ge substrate. The peak transmittance was 98.3%, and the average transmittance increased from 48.4% to 96.2% when double-sided coating was used. This LWIR AR coating with LaF3 passed various environmental, durability, and mechanical properties tests while maintaining good optical properties.

Laser heat treatment is a high-temperature material-treatment technique that can heat rapidly and can work in a broad temperature range; it heats at a rate of up to 104℃/s. The traditional methods of measuring contact temperature cannot satisfy the measurement requirements of a rapid temperature-rising range and heating process. In this study, a noncontact image processing-based infrared CCD method was designed to measure the surface temperature of materials. Moreover, a method of segmented measurement was developed to solve the problem of the wide temperature measurement range. For the rapid heating rate, a computer-controlled fast adaptive exposure-time algorithm was proposed, and the temperature-grayscale curve was strictly calibrated to improve the measurement accuracy. The experimental results showed that the proposed method is a low-cost, high-stability, high-precision, simple, and effective temperature measurement technique. This paper presents a practical improvement technique for measuring the surface temperature of materials during the laser heat-treatment process.

Infrared chalcogenide optical glass is a material that is opaque to visible light. The method outlined in GB/T 7962 cannot be used to test its striae. Herein, test methods based on parallel light projection are presented to facilitate striae detection and characterization. The corresponding test equipment (chalcogenide glass samples) are developed and tested for striae and grayscale and modulation transfer function (MTF); further, the relationship between grayscale and MTF is investigated. Subsequently, the characterization method of striae severity with grayscale is proposed. The results show that the defects and distribution of the inner striae can be clearly detected after imaging in the near infrared band by parallel light projection. The mid-infrared MTF can qualitatively characterize the effect of striae on imaging quality. The striae can be used to quantitatively characterize the internal striae condition of the sample and reflect the effect on imaging quality. This paper provides a reference for the subsequent determination of the internal quality of infrared chalcogenide optical glass.

This paper proposes an infrared and visible image fusion method based on gray energy difference for two purposes: one, to obtain the prominent target features in an infrared image for extracting the important details in the visible image, and two, to solve the problem that the target information in traditional algorithms is not sufficiently prominent and that the details and textures are often missing. In this method, first, the target feature in the infrared image is detected by a target extraction algorithm based on gray energy difference. Second, infrared and visible images are decomposed to high and low frequencies using a non-subsampled contourlet transform (NSCT). Third, the gray energy difference map is used as the fusion weight to fuse the low-frequency parts of the infrared image and the visible image. The high-frequency part is fused by the rule of weighted variance. Finally, the NSCT inverse transform is used to fuse the high-frequency and low-frequency coefficients to obtain the final fused image. In this study, three groups of classical infrared and visible images are selected for fusion experiments and compared with other methods through subjective vision and objective indicators. Experimental results show that the algorithm can effectively highlight target information, improving contrast and sharpness and retaining texture details.

In view of the problem of detecting and tracking maneuvering small targets at low signal-to-noise, a track-before-detect algorithm based on sequential Monte Carlo probability hypothesis density filtering for Jump-Markov systems (JMS-SMC-PHD) is presented. Under the condition of an unknown number of maneuvering targets and unknown models, the algorithm achieves track-before-detect of small maneuvering targets by using measurement data from infrared sensors directly, adding a variable that denotes the dynamics model of the target, and using a Markov model probability transfer matrix combined with an SMC-PHD filter. Simulation results show that the proposed method can effectively implement target detection and tracking performance.

The task of scene prediction is studied to improve the decision-making speed of driverless vehicles for reducing the probability of traffic accidents at night. A dual-channel encoding night scene prediction network is proposed based on a convolutional long-short term memory network. First, the temporal features of infrared video sequences and the spatial features of infrared images are extracted by the temporal and spatial sub-networks, respectively. Second, spatial-temporal features obtained by the fusion network are input into the decoding network to predict future frames of infrared video. This is an end-to-end network and can predict multiple frames. The experimental results show that the proposed network is more accurate in night scene prediction and can predict images 1.2 s in the future with a fast prediction speed of0.02s/frame, which fulfills the real-time requirement.

Eddy current pulsed thermography is an emerging nondestructive testing technique that has been widely used for flaw detection in metallic materials. Typically, its performance is evaluated through hit/miss analysis. However, the traditional method of analyzing hit/miss requires considerable experimental data, which is time-consuming and expensive. In this study, a model-assisted method based on back-propagation neural networks (BPNNs) for hit/miss prediction was developed to minimize the need for additional experimental tests. Thirty sets of metal specimens with fatigue cracks of different lengths were fabricated; 15 experimental groups were subjected to different detection conditions. Subsequently, three sets of the probability of detection (POD) curves were plotted, and the effects of the different detection conditions on the POD were analyzed. Finally, a prediction model of the hit/miss based on the BPNN was constructed, and the hit/miss prediction was realized. The results showed that under different detection conditions, the proposed framework could complete the hit/miss prediction with an error of zero.

A segmentation method was developed for combining an improved adaptive genetic algorithm with a two-dimensional maximum entropy algorithm based on the image features of ultrasound infrared thermography detection for accurate and rapid segmentation of the target defect region to recognize defects of defect recognition in images. First, the infrared image was processed to obtain a denoised image. Next, the image was divided into the target and background regions using a two-dimensional maximum entropy algorithm; the segmentation speed was improved by combining it with the improved adaptive genetic algorithm. The experimental results showed that this method can effectively filter image noise. Compared with an exhaustive method and the two-dimensional maximum entropy segmentation based on a simple genetic algorithm, the proposed algorithm has better segmentation speed and accuracy.

This paper proposes a method of extracting fault regions from infrared images of electronic equipment using a pulse-coupled neural network (PCNN); the method is based on iterative clustering. The PCNN model is used as the kernel method for image processing. Several parameters are first determined, and then hierarchical clustering is introduced to enable the PCNN model to segment an image into multiple regions on the basis of the inner similarity. In addition, the cluster centers are computed and sorted from large to small to find the pulse region with the highest brightness. The merging processing is final carried out with its neighboring region and the measure of similarity. The method thus improves the ability of the PCNN model to segment infrared images efficiently and effectively identifies thermal fault regions. Experimental results show that the proposed method exhibits good segmentation performance and is suitable for processing infrared images of thermal fault regions.