Aiming at the poor suppression ability for the high-frequency noise in Huber-MRF prior model and the excessive punishment for the high frequency information of image in Gauss-MRF prior model, an adaptive regularization HL-MRF model was proposed. The method combined low frequency function of Huber edge punishment with high frequency function of Lorentzian edge punishment to realize a linear constraint for low frequency and a less punishment for high frequency. The model gained its optimal solution of parameters by using adaptive constraint method to determine regularization parameter. Compared with super-resolution reconstruction methods based on Gauss-MRF prior model and Huber-MRF prior model, the method based on HL-MRF prior model obtains higer Peak Signal-to-Noise Ratio (PSNR) and better performace in details, therefore it has ceratin advantage to suppress the high frequency noise and avoid excessively smoothing image details.

Since the triangle identification algorithm commonly utilized in star sensitive system is of high data redundancy and low recognition speed, especially initial recognition speed, a concentric circles-based star pattern recognition algorithm of large Field Of View (FOV) was proposed. After analyzing the information of star map to acquire its main star, draw eight concentric circles around the main star at some certain radiuses, then figure out the number of stars in each annulus based on the coordinates to obtain the distributional vector of companion stars. Construct the navigation star feature database from the base database with the utilization of the same method, so as to process the pattern matching with the distributional vector to acquire star pattern recognition result. The vectors in the database will be sorted by the first dimensional element in order to accelerate the process of recognition. The simulation results show that this algorithm needs much less storage space of navigation star feature database, and possesses good real-time and noise resistance, and high recognition rate. It takes 95.3μs recognition time to achieve more than 88.9% accuracy, and it also can be integrated with other recognition algorithms and performance in different stages to realize more efficient and accurate celestial navigation.