Robust motion blur kernel parameter estimation for star image deblurring

Under dynamic conditions, the star images may be blurred and result in the decrease of attitude measurement accuracy of the star sensor. To estimate blur kernel parameters needed for star image deblurring, including blur angle and blur length, a method based on sparse representation, hyper-Laplacian priors, and ensemble neural network is proposed. First, under the constraint of the hyper-Laplacian image prior, the blur angle is estimated by using the quasi-convex characteristics between the sparse representation coefficients and the blur angle. Then, the ensemble back-propagation neural network trained by the bagging method is exploited to estimate the blur length. Finally, we recover the star image by a non-blind deblurring algorithm. To validate the proposed algorithm, we test the algorithm on star images and compare the algorithm with several existing deconvolution algorithms. The results reveal that our approach outperforms other algorithms in terms of effectiveness and robustness.