Short-wave infrared imaging has the advantages of clear target detail expression, strong target identification ability, strong adaptability to haze climatic conditions and dust and smoke application environment, and can realize hidden imaging, low light level night vision detection, so more and more short-wave infrared imaging research is carried out. Based on the short wave infrared images as the goal, to carry out the short-wave infrared image colorization method based on color migration study, put forward an improved way of automatic unsupervised grayscale image coloring method, based on the depth of the convolutional neural network learning coloring method, and the global image of the transcendental and extracted from the whole image to calculate the local image features from image patches, The global prior provides image-level information, while the local feature represents the local texture or object at a given location. By combining these two features, the image is automatically colored to realize the color of short-wave infrared image, and the image quality evaluation after color is given. The experimental results show that the method has no loss of color image details, the image color information distribution is reasonable, high definition, obtained a new visual effect image, for target detection and recognition, key area monitoring to provide a more visual effect of target detection and recognition and scene monitoring means.
In this paper, a convenient, fast and efficient mid-wave infrared imaging spectrum acquisition system based on AOTF is designed and developed, which is used for target spectrum acquisition and image display. The experimental results show that the spectrometer can successfully acquire 3 μm to 5 μm mid-wave spectrum data, which lays a solid foundation for the acquisition, analysis and application of the target medium-wave spectrum data. This would provide system support for military reconnaissance, camouflage detection, stealth effect detection and evaluation, battlefield defense, etc.
Under the condition of information war, combat equipment is faced with the serious threat of "discovery is destruction". In the all-weather and all-day reconnaissance environment, the infrared spectrum characteristics of the target and its effective control become the key to change detectability and improve survivability.In view of the important value of infrared spectrum characteristics in battle, this paper carries out data collection and comprehensive processing of MWIR spectrum in equipment testing. Firstly, combining with typical task process, a data collection method is established, which forms a theoretical analysis, atmospheric transmission, data collection, radiometric calibration model. Then, the radiation data processing research is carried out. Taking the digital image as the original data, the radiation emittance is obtained by radiation calibration. Based on the transmittance and radiation model, the calculation method of apparent temperature is established. To verify the method proposed in this paper, combined with helicopter infrared image data, the distribution of MWIR radiation field and temperature field of the equipment during takeoff, landing and dynamic flight is obtained, reaching 5% calibration measurement accuracy.The feasibility and validity of the method are verified.In this paper, the collection and processing of infrared spectrum characteristics is a useful exploration to improve the quality and efficiency of equipment inspection capability. It has practical value for improving equipment data and fully characterizing equipment capability in the actual environment.
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