With the continuous development and application of dual-band infrared focal plane detector, the demand for dual-band infrared image fusion algorithm which can be applied in engineering is also increasing. Compared with mid-wave infrared (wavelength of 3μm~6μm) and long-wave infrared (wavelength of 8μm ~14μm), short-wave infrared (wavelength of 0.35μm ~3μm) has a greater difference in image performance. Due to the radiation characteristics, short-wave infrared images are more similar to visible images, with more environmental radiation information and detailed features. Therefore, different from the medium and long wave dual-band infrared image fusion, the design of medium and short wave dual-band infrared image fusion is more inclined to retain the rich details of short-wave infrared as much as possible while making the thermal radiation of the target itself more prominent. In this paper, a short-medium wave color fusion algorithm based on the helical mapping of differential features is proposed. The algorithm can map the color of the image to the area that is consistent with the observation habit of human eyes according to the target and scene, which has a richer color hierarchy and retains more short-wave detail features. The algorithm proposed in this paper has been simulated and analyzed in several scenes, and the fusion result is better than the mainstream color fusion algorithm in visual effect, and the objective indicators also have certain advantages. At the same time, the embedded design, simulation and synthesis of the algorithm are carried out on the embedded platform based on FPGA K7 series. At the same time, it shows that the algorithm has very high real-time performance and very low resource occupation, and has the value of engineering realization.
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.
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