This paper proposes a space false alarm target detection method based on star information. The central idea is that if a non-star target coincides with the same projected star for several consecutive frames, it is considered as a false alarm target. The implementation steps are: First, project the stars in the star catalog to the image plane. The ID numbers of stars in the star catalog is placed in the data structure of projected star points. Second, based on the matching results of the projected star points and the image points, the image points matching with the stars is obtained, and the non-star target is identified. Third, compare the non-star target with the matching points in each frame. If the Euclidean distance is less than a certain threshold, then the non-star target will be added to the predetermined false alarm target queue. The ID number is placed in the predetermined false alarm target data structure. Fourth, if the ID number of the predetermined false alarm target is the same in several consecutive frames, the target will be regarded as a confirmed false alarm target, and it will be removed from the non-star target queue. Simulations show that the method can effectively realize the identification and elimination of false alarm targets and reduce the false alarm rate.
In order to improve image processing quality and boost processing rate, this paper proposes an real-time automatic
image enhancement algorithm. It is based on the histogram equalization algorithm and the piecewise linear enhancement
algorithm, and it calculate the relationship of the histogram and the piecewise linear function by analyzing the histogram
distribution for adaptive image enhancement. Furthermore, the corresponding FPGA processing modules are designed to
implement the methods. Especially, the high-performance parallel pipelined technology and inner potential parallel
processing ability of the modules are paid more attention to ensure the real-time processing ability of the complete
system. The simulations and the experimentations show that the algorithm is based on the design and implementation of
FPGA hardware circuit less cost on hardware, high real-time performance, the good processing performance in different
sceneries. The algorithm can effectively improve the image quality, and would have wide prospect on imaging
processing field.
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