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With the development of infrared dual-band focal plane detector in the field of engineering application, the research on image fusion algorithm of dual-band infrared thermal imager based on engineering application becomes more and more important. Because the human eye can only distinguish dozens of gray scales, but can distinguish thousands of color scales. At the same time, gray images can only store one-dimensional data, while color images can contain three-dimensional data, which expands the amount of information by tens of thousands of times, and is easier to be applied to the processing and processing of machine vision related work in the field of detection, search, recognition and tracking. Therefore the research on color image fusion is becoming more and more important. At present, color image fusion is the main development trend of infrared dual-band image fusion. The mainstream color image fusion can be divided into four categories: color mapping, color transfer, color lookup table and neural network. The above four algorithms are difficult to take into account the color richness, environmental adaptability and human eye observation comfort. In this paper, the above problems, this paper proposes a class on medium wave infrared and long wave band color map image fusion algorithms, the algorithm is based on each source image pixel and image mean differences, the differences between the information more reasonably map to color print, the final fusion image has a better image perception and environment adaptability, and preserve more details. A typical dual-band infrared thermal imager application Scene was selected, and the subjective image comparison and objective index analysis were carried out by algorithm simulation and other mainstream color fusion algorithms, which proved that the algorithm was effective and feasible.

Infrared images typically contain obvious dark-corner noise. It is a challenging task to eliminate such noise with the acceptable computation overhead and time overhead. In this paper, we introduce an effective dark-corner noise removal algorithm consists of two consecutive processing procedures. Firstly, in order to effectively filter dark-corner noise with as few as frames of infrared images, the proposed algorithm accumulates the low-frequency pixels during the several different frames of infrared images and eliminates the dark-corner noise by subtracting this parameter from the original infrared image. Then, this algorithm sets several detection windows for dark-corner noise to obtain another additive correction parameter and subtract this parameter from the original infrared image. We demonstrate the effectiveness of our algorithm from experimental perspective.

With the sustaining development of application requirements in infrared technology, modern infrared imaging system demands high frame rates, wide dynamic range, high spatial resolution and high sensitivity. Because it is impossible to integrate hundreds of pF capacitor in the limited area of detector pixel, the integration time of infrared staring imaging system will be restricted. Therefore, the underutilization of detector performance is unavoidable. Specially, long wave infrared detector must accommodate stronger infrared signal, and the integration capacitor is more easily saturated. For the sake of resolving the restriction of integration capacitor, an infrared image superframing technique based on high-speed digital transmission circuit is presented in this paper. Meanwhile, the mass raw data high-speed transmission from detector to imaging circuit is also capable via the proposed technique. With the usage of the technique, the signal to noise ratio (SNR) of infrared imaging system will be improved, and the dynamic range of infrared imaging system will be also extended. The theory analysis and results of simulation demonstrate that the proposed method is feasible and effective.