基于增强拉东变换及稀疏先验的模糊星斑复原

Blurred star restoration based on improved Radon transform and sparse prior

为了提高动态条件下星光导航系统星斑质心定位精度,本文提出了一种新的模糊星斑复原算法。首先,分析了星斑图像退化模型,得出了星斑质心定位精度与星斑信噪比呈正相关的关系。对于载体角运动和载体振动两个因素引起的模糊星斑复原过程分成粗级复原和精级复原两个步骤。接着分析了噪声对拉东(Radon)变换的影响,提出基于灰度拉伸的改进Radon变换算法进行粗复原。最后,根据清晰星斑的梯度分布稀疏先验正则化,利用迭代盲复原算法对星斑进行精复原。仿真实验结果表明,本文提出的两步模糊星斑复原算法较传统算法复原后星斑峰值信噪比(PSNR)提高30%,质心定位精度提高55%。

A New blurred star spot restoration algorithm was proposed to improve the accuracy of the centroiding of star of the star navigation system under dynamic conditions in this paper. Firstly, the star blurring model was analyzed, and the conclusion that the accuracy of the centroiding of the star increased with the signal to noise ratio（SNR） was obtained. The recovery of the blurred star was divided into coarse and fine steps for the carrier angular motion and vibration. The effect of noise on Radon transform was analyzed, and an improved Radon transform algorithm for gray stretching was proposed. Finally, the iterative blind restoration algorithm was used to perform the precise restoration of star spot based on the sparse prior regularization of the gradient distribution of the clear star spot. The simulation and experimental results show that the peak signal to noise ratio （PSNR） of the two step blurred star spot restoration algorithm is improved by 30% and the precision of centroid positioning is improved by 55%, compared with the traditional algorithm.