The Gen 2 image intensifier is keeping a parallel technology development status with the Gen 3 image intensifier and the debate over the generation division and performance upgrade is also accompanying their development. Over time, as substantial breakthroughs for multi-alkali photocathode in the extended spectral range occur, as well as the development of filmless micro- channel plates(MCP) in some GaAs photocathodes image intensifiers, the term “Generation” for image intensifiers has arguably lost meaning. However, there is still lack of accurate comparison assessments for the filed performance that both generations have achieved. Though the existing military specification cannot fully reflect the upgraded image intensifier characteristic and its actual field performance, standards updates as well as investigations of the link between these characteristic parameters and the actual field performance are necessary.

Based on introducing a calculation method for the limit of geometric detection range, the structure of vehicle-based photoelectric masts with a straight arm or a crank arm is presented. Photoelectric detection system in the application of vehicle mast can enhance the situation awareness ability of the system. The market demands of the vehicle-based optoelectronic mast are expected to increase with the growing maturity of its technology and continuous expansion of its application. The current status and application prospect of vehicle-based photoelectric mast technology at home and abroad are summarized.

To get low transmittance in mid-infrared atmospheric windows (3-5.m) and far-infrared atmospheric windows (8-14.m), we designed a double frequency infrared frequency selective surface (FSS). This FSS is composed of two ring structures – the outer side of the structure is a hexagon and inner side is a circle. The simulation results of CST electromagnetic software show that the average transmittance of the FSS in both mid and far infrared atmospheric windows is less than 5%; in addition, the two stopbands in infrared wavelengths are realized. The filtering mechanism of the frequency selective surface is analyzed based on the method of surface current model analysis. The structure forms a symmetrical current mode through the coupling between the unit in the screen, which enhances the scattering-field and decreases the transmission rate, forming a stopband in the corresponding band. The simulation results show that the structure has polarization stability and good angle stability for TE electromagnetic waves with different incident angles. In addition, the dielectric layer thickness and loss tangent have little effect on transmission properties, and dielectric constant has a great effect on transmission properties.

The successful launch of the GF-2 satellite indicates that China's remote sensing satellites have entered the era of high spatial resolution of the sub-meter level. Remote sensing images will play an important role in quantitative inversion, ground object recognition, and change analysis. The accuracy of its atmospheric correction is an important factor that affects its quantitative application. Due to the lack of a short-wave infrared band in GF-2, it is impossible to use a dark pixel method for atmospheric correction. A method of atmospheric correction for the GF-2 image based on a radiation transfer model is proposed. The atmospheric correction coefficient lookup table is established by using 6S (Second Simulation of the Satellite Signal in the Solar Spectrum) radiation transfer model. The aerosol optical thickness (AOT) is retrieved by combining synchronous MODIS image data with an improved dark pixel method. The atmospheric correction parameters are determined to eliminate the influence of absorption and scattering of atmospheric molecules and aerosols in the GF-2 image and to achieve atmospheric correction of GF-2 data. Dunhuang radiation correction field with a flat and uniform surface is selected as the experimental area. The accuracy of the correction results is evaluated by synchronous measured data, and the normalized difference vegetation index (NDVI) before and after atmospheric correction is compared. The results show that the minimum relative error is only 0.9%. The image data after atmospheric correction can accurately reflect the reflection characteristics of ground objects. NDVI after atmospheric correction greatly enhances the contrast of vegetation information and highlights the ability of vegetation information discrimination of the GF-2 satellite sensor.

To study the environmental adaptability of OLED displays in tropical rainforest, OLED displays are placed into the storeroom of experimental station in Xishuangbanna, and they are observed and tested regularly. After one-year-exposure, the surface of OLED displays appears pinhole, black point, and obscission. In addition, the area of luminescence and brightness were reduced. Investigation revealed: the sealing of OLED displays is destroyed in tropical rainforest, subjected to the long term alternating stress caused by temperature and humidity, thus led to the failure of OLED with oxygen and moisture in the interior of displays. Therefore, an improved sealing is the key to the service life of OLED displays in tropical rainforest.

Owing to the abnormal response of the thermal infrared bands of the visible infrared multi-spectral imager (VIMI) aboard GF-5 satellite it is challenging to guarantee the radiometric calibration accuracy; considering this fact, we conducted a supplementary test of ground radiometric calibration. We obtained that the response sensitivity of the thermal infrared bands to the change of instrument cavity temperature and the focal plane temperature was the main reason for this abnormal phenomenon. Furthermore, we analyzed and summarized the law of output signal changing with instrument cavity temperature and focal plane temperature. An absolute radiation calibration algorithm based on instrument temperature and focal plane temperature revise was proposed. It was successfully applied to laboratory absolute radiation calibration of thermal infrared bands of VIMI. The results show that the uncertainty of calibration is 1.36%.

The main task of infrared image dehazing algorithms is to solve the problems of low visibility and blurring in infrared images; these problems arise from Mie scattering. However, current infrared image dehazing algorithms poorly estimate the dark transmittance of infrared images. Hence, in this study, an infrared image dehazing algorithm is developed based on the dark primary color prior of the haze-line. First, the Hough transform was employed to estimate the atmospheric illumination. Second, a dark primary color prior was employed to address the failure of the haze-line dehazing method in some scenarios. The transmittance was estimated by assuming that the dark end of the haze-line corresponds to the real color, and a transmittance map was obtained. To remove noise in the transmittance map, total variation regularization was used; thus, the transmittance map was optimized. The experimental results obtained using LTIR, a public infrared dataset, as the test dataset show that the proposed algorithm can enhance the clarity of infrared images without affecting the distribution of infrared radiation; in addition, the results show that the proposed algorithm enhances infrared images corresponding to various scenes. The proposed method accurately estimates transmittance and effectively dehazes infrared images.

In this paper, an infrared-image-based algorithm is proposed for the detection of dim and small targets in complex backgrounds. The proposed algorithm is based on the contrast mechanism of the human visual system. First, an infrared image was preprocessed, and isolated noise points in the image were removed via median filtering. The processed image was then subjected to difference-of-Gaussians filtering to suppress large-area highlighted areas in the image. Finally, an improved local contrast algorithm was used to remove the highlighted edge regions and eliminate the high suspect target to achieve the detection of dim and small targets in complex backgrounds using infrared images. Experimental results show that compared with the traditional LCM algorithm, top-hat algorithm, TDLMS algorithm, and infrared patch-image model, the proposed algorithm is more advantageous with regard to the false alarm rate, correct detection rate, detection time, etc. It also has the characteristics of a high detection rate, low false alarm rate, good robustness, and short running time.

The traditional infrared dim and small target detection algorithm is generally implemented by digital signal processing, which is complex and has poor real-time performance. In this study, a features from accelerated segment test (FAST) adaptive-threshold algorithm based on a field programmable gate array (FPGA) is proposed to detect infrared dim and small targets. Based on the characteristics of FPGA parallel processing, the hardware acceleration of the algorithm was realized by a pipeline design. The improved adaptive threshold method can generate appropriate thresholds according to different environments and avoid the loss or redundancy of dim and small infrared targets that is caused by improper threshold selection. Finally, two different groups of infrared images were used for an experiment. The results show that the algorithm can detect dim and small targets in an infrared image in real time and can achieve a high detection rate and low false alarm rate, thereby meeting the requirements of real-time performance and effectiveness.

Bumps and sloshing generated during aircraft flights disrupt the capture of a sequence of infrared images. This affects the subsequent observation of the images, as well as the identification, location, and tracking of the target. In this paper, a method for real-time electronic image stabilization of airborne infrared images is proposed based on an improved Harris corner method. First, an improved Harris corner response function was combined with a distance constraint to perform corner detection. This method ensures that a sufficient number of corner points with uniform distribution can be detected, even for infrared images with poor image quality. The proposed key-frame reference method was then combined with the multi-scale pyramid optical flow algorithm; furthermore, the detected corner points were applied to the aforementioned combination, thereby achieving tracking matching and motion estimation. Consequently, electronic image stabilization of the airborne infrared images was realized. Using this method, multiple sets of jittery infrared image sequences were processed; the image size was 640.512. Experimental results show that the proposed algorithm exhibits a better corner detection effect than the traditional Harris corner detection algorithm. It can remove jitter from the airborne infrared image sequence and ensure real-time processing at an acquisition rate of 50 fps.

Depth estimation from monocular infrared images is required for understanding 3D scenes; moreover, it could be used to develop and promote night-vision applications. Owing to the shortcomings of infrared images, such as a lack of colors, poor textures, and unclear outlines, a novel deep conditional random field network (DCRFN) is proposed for estimating depth from infrared images. First, in contrast with the traditional CRF(conditional random field) model, DCRFN does not need to preset pairwise features. It can extract and optimize pairwise features through a shallow network architecture. Second, conventional monocular-image-based depth regression is replaced with multi-class classification, wherein the loss function considers information regarding the order of various labels. This conversion not only accelerates the convergence speed of the network but also yields a better solution. Finally, in the loss function layer of the DCRFN, pairwise terms of different spatial scales are computed; this makes the scene contour information in the depth map more abundant than that in the case of the scale-free model. The experimental results show that the proposed method outperforms other depth estimation methods with regard to the prediction of local scene changes.

The dacron crystal structures (ν-crystal-dacron) were studied by infrared (IR) spectrum, for instance, ν-crystal-1-dacron(1335 cm.1), ν-crystal-2-dacron(969 cm.1), and ν-crystal-3- dacron(847 cm.1). In addition, the dacron thermostability was studied by temperature-dependent infrared(TD-IR) spectrum. We obtained that the corresponding absorption intensity and frequency of dacron ν-crystal-dacron were changed (353-393 K) because its crystal structure was destroyed by the temperature. We further studied the glass transition of dacron ν-crystal-dacron was by two- dimensional infrared(2D-IR) spectrum. During heating, the dacron macromolecular chain showed different motion states. The dacron molecules froze in molecular motion before the glass transition temperature (313-343 K); however, these molecules nearly entered a high elastic state after the glass transition temperature (353-393 K). This study demonstrated the key roles of three-step infrared spectrum (including IR spectrum, TD-IR spectrum, and 2D-IR spectrum) in the analysis of structure, thermostability, and glass transition of the important polymer material(dacron).

Based on the simultaneous temperature difference of the HDPE film defect area and the intact area, defects of HDPE film were detected with infrared thermal imaging technology. Under the action of a continuous heat source: i) infrared images are collected for defects of different areas and shapes, ii) the temperature of different areas of the film surface is recorded, iii) the temperature changes of different locations and the shadow area of infrared defects with time are analyzed, and iv) finally, the temperature characteristic curve and the best defect detection time limit were studied. The experimental results show that the temperature trends of the defect and complete area are the same the whole time; however, there is a synchronous temperature difference, and the infrared temperature image acquisition time is clearly in the 10-20 mins. It can be regarded as the best detection time domain of the defect. When the thermal image acquisition time is 13 mins, the infrared image edge contour is the same as the real defect contour; therefore, it is the optimal detection time point.