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Infrared imaging system compared with visible light imaging system, infrared imaging system can operate in all-weather conditions with high anti-interference ability and the ability to penetrate smoke and haze. The current infrared technology still has the disadvantage of low signal-to-noise ratio and lack high frequency detail information. The most direct way to improve the infrared imaging system is to improve the hardware design by increasing the size of the photoreceptor and the size of the image source, but the manufacturing process is complicated, the cost is high, and the improvement effect is limited. With the development of the field of computer vision, deep learning has become the most effective solution for super-resolution reconstruction[6]. Therefore, in this paper, a Video frame Infrared image super-resolution reconstruction method IVSR (Infrared Video super-resolution) is designed based on the deep learning method and the dual-path operation mode combining the single-frame and multi-frame super-resolution reconstruction algorithms. The innovation of IVSR net lies in that after the optical flow module extracts the inter-frame information of each frame and the current frame, the sub-pixel convolution layer of the multi-frame image fusion module can effectively utilize the sub-pixel information. IVSR can be regarded as two reconstruction processes, the first stage is the output of high-resolution information from the optical flow motion estimation module to the projection module, and the second stage integrates the high-resolution information of each projection module for fusion reconstruction. This method can effectively improve the visual effect of IVSR reconstruction, effectively solve the problem of poor reconstruction quality due to the lack of high frequency detail information. Compared with traditional reconstruction algorithms and other typical deep learning algorithms, reconstructed images of IVSR are more exquisite, with more prominent high-frequency details, no image distortion and significant advantages in objective evaluation indicators.
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