A High Accuracy Semi-Blind Restoration Algorithm for a Hypersonic Star Image

被引:0
作者
Yang B. [1 ]
Li J.-X. [1 ]
Yang Z.-H. [2 ]
Liu Y.-S. [3 ]
机构
[1] School of Astronautics, Beihang University, Beijing
[2] School of Instrumentation Science & Optoelectronics Engineering, Beihang University, Beijing
[3] Beijing Mechanical and Electrical Engineering General Design Department, Beijing
来源
Yuhang Xuebao/Journal of Astronautics | 2019年 / 40卷 / 04期
关键词
Aero-optics; Celestial navigation; Hypersonic vehicle; Image restoration; Regularization;
D O I
10.3873/j.issn.1000-1328.2019.04.007
中图分类号
学科分类号
摘要
A semi-blind restoration algorithm of a hypersonic star image based on the regularization method is proposed, which can be used in the celestial navigation of a hypersonic vehicle disturbed by the aero-optical effect and motion blur in order to solve the problem of the star recognition and navigation accuracy. In the algorithm, the pre-processing operations are carried out firstly, such as denoising and star-point initilal extration depend on the characteristics of hypersonic star images. The useful imformation of kernel is extracted from image and fused to denoise the kernel. Based on the sparse prior of both the gray image and its gradient of the star image, a regularized nonblind restoration model for a hypersonic star image is proposed to estimate the clear image with the help of the Bregman iterative algorithm. The algorithm is compared with the traditional star image restoration algorithm and the other state-of-art regularization restoration algorithms in experiments. The results show that the proposed algorithm has the best recovery ability, which can obviously improve the accuracy of star recognition. Thus, it can be used to greatly improve the adaptability and accuracy of the celestial navigation of a supersonic vehicle in the stratosphere. © 2019, Editorial Dept. of JA. All right reserved.
引用
收藏
页码:425 / 434
页数:9
相关论文
共 12 条
[1]  
Pond J.E., Sutton G.W., Aero-optic performance of an aircraft forward-facing optical turret, Journal of Aircraft, 43, 3, pp. 600-607, (2015)
[2]  
Gao Q., Yi S.H., Jiang Z.F., Et al., Temporal evolution of optical path difference of a supersonic turbulent boundary layer, Chinese Physics B, 22, 1, (2013)
[3]  
Hong H.-Y., Zhang T.-X., Restoration of turbulence-degraded images using anisotropic and nonlinear regularization, Journal of Astronautics, 25, 1, pp. 5-12, (2004)
[4]  
Hong H.-Y., Zhang T.-X., Yu G.-L., Iterative multi-frame restoration algorithm of turbulence-degraded images based on passion model, Journal of Astronautics, 25, 6, pp. 649-654, (2004)
[5]  
Fergus R., Singh B., Hertzmann A., Et al., Removing camera shake from a single photograph, Acm Transactions on Graphics, 25, 3, pp. 787-794, (2006)
[6]  
Krishnan D., Fergus R., Fast image deconvolution using hyper-Laplacian priors, International Conference on Neural Information Processing Systems, (2011)
[7]  
Ohkoshi K., Goto T., Hirano S., Et al., Blind image restoration based on total variation regularization of blurred images, The 1st IEEE Global Conference on Consumer Electronics, (2012)
[8]  
Pan J., Sun D., Pfister H., Et al., Blind image deblurring using dark channel prior, Computer Vision and Pattern Recognition, (2016)
[9]  
Krishnan D., Fergus R., Fast image deconvolution using hyper-Laplacian priors, International Conference on Neural Information Processing Systems, (2009)
[10]  
Krishnan D., Tay T., Fergus R., Blind deconvolution using a normalized sparsity measure, Computer Vision and Pattern Recognition, (2011)