A Real-Time Star Tailing Removal Method Based on Fast Blur Kernel Estimations

The number of star points and the accuracy of star centroid extraction are the key factors that affect the performance of the star sensor under high dynamic conditions. The motion blur results in the star point trailing, which consequently leads to the decline of star centroid extraction accuracy and in some cases lead to extraction failure. In order to improve the dynamic performance of the star sensor, in this work, we propose a real-time star trailing removal method based on fast blur kernel estimation. First, in order to minimize the influence of noise on parameter estimation, we use principal component analysis (PCA) in the dual-frequency spectrum domain to estimate the angle of blur kernel. In addition, an adjustable weighting method is proposed to estimate the length of blur kernel. So, we are able to quickly estimate the high-precision blur kernel based on a single degraded image. Moreover, an area filtering method based on the hyper-Laplacian prior recovery algorithm is also proposed. This algorithm quickly reconstructs the star points in the tracking windows and effectively removes the star point tailing in real time. The computational efficiency of the proposed algorithm is 5 times superior to the traditional method and 15 times superior to the existing accelerated iterative method. The experimental results show that the proposed algorithm removes the trailing star quickly and effectively, under low SNR. In addition, the proposed method effectively improves the number of extracted star points and the accuracies of star centroids.