This paper builds a real-time infrared target detection system with a visual positioning function based on FPGA, a visual camera, and an infrared camera. Firstly, the whole system is built based on FPGA. The design and implementation of the infrared image acquisition module, a data storage module, SD card module, and image display module are completed. Ping pong operation is used for the data storage modules to realize video stream transmission. The experiment results show that the system built in this paper can collect and display the target in real-time. To improve the system performance, several image processing algorithms are proposed, including an improved median filtering algorithm, linear transformation, and Laplacian sharpening algorithm; a combined algorithm of histogram equalization, Gamma transform, and Laplacian sharpening; a target detection algorithm combined with threshold segmentation and a background difference algorithm; and a visual localization algorithm. Software simulation and FPGA hardware implementation results show the effectiveness of the proposed algorithms.
In recent years, with the increasing demand for multi-source information fusion technology, infrared and visible image fusion technology has been developed rapidly and become an important research direction in the optical information processing field. Combing the advantage of LabVIEW and MATLAB, we proposed a new infrared and visible image fusion system in this paper. An infrared and visible light video image fusion system based on LabVIEW and MATLAB is built. To solve the problem of low infrared image resolution and poor image quality, we proposed an infrared image enhancement algorithm. Experiments result show that the algorithm can enhance the edge features of the infrared image while retaining the internal details of the image. A filtering layered fusion algorithm based on wavelet transform and weighting method is proposed. The algorithm uses anisotropic filtering to decompose the image, calculate each layer's fusion weight, and use the wavelet transform and weighting fusion method to obtain the fused image. Both simulation and actual system experiments prove the effectiveness of the system design and the algorithm.
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