Motion-blurred Star Image Research Articles

Motion-blurred star image restoration based on multi-frame superposition under high dynamic and long exposure conditions

Motion-blurred star image restoration based on multi-frame superposition under high dynamic and long exposure conditions

Motion Blurred Star Image Restoration Based on MEMS Gyroscope Aid and Blur Kernel Correction.

Under dynamic conditions, motion blur is introduced to star images obtained by a star sensor. Motion blur affects the accuracy of the star centroid extraction and the identification of stars, further reducing the performance of the star sensor. In this paper, a star image restoration algorithm is investigated to reduce the effect of motion blur on the star image. The algorithm includes a blur kernel calculation aided by a MEMS gyroscope, blur kernel correction based on the structure of the star strip, and a star image reconstruction method based on scaled gradient projection (SGP). Firstly, the motion trajectory of the star spot is deduced, aided by a MEMS gyroscope. Moreover, the initial blur kernel is calculated by using the motion trajectory. Then, the structure information star strip is extracted by Delaunay triangulation. Based on the structure information, a blur kernel correction method is presented by utilizing the preconditioned conjugate gradient interior point algorithm to reduce the influence of bias and installation deviation of the gyroscope on the blur kernel. Furthermore, a speed-up image reconstruction method based on SGP is presented for time-saving. Simulated experiment results demonstrate that both the blur kernel determination and star image reconstruction methods are effective. A real star image experiment shows that the accuracy of the star centroid extraction and the number of identified stars increase after restoration by the proposed algorithm.

Fast restoration of star image under dynamic conditions via lp regularized intensity prior

This paper is an in-depth look at the problem of removing the blur from a complex motion-blurred star image. Accordingly, a simple yet effective lp(0<p≤1)-regularized deblurring method based on stars image intensity is proposed. The model builds on the principle that the intensity of clear star image is in accordance with Laplacian distribution or generalized p Gaussian distribution. Further, two algorithms are introduced to solve the ensuing non-smooth (p=1) or non-convex (p<1) constrained optimization problem. Simulations and actual star image restoration experiment are implemented to demonstrate that the centroids extraction accuracy of the proposed method is higher than 0.1 pixel, the running time is 3 to 5 times better than Richardson–Lucy filter or other methods based on image gradient constraint, and the peak signal to noise ratio (PSNR) of recovered star images excel results of several other image deconvolution methods.

Region-confined restoration method for motion-blurred star image of the star sensor under dynamic conditions.

Under dynamic conditions, the centroiding accuracy of the motion-blurred star image decreases and the number of identified stars reduces, which leads to the degradation of the attitude accuracy of the star sensor. To improve the attitude accuracy, a region-confined restoration method, which concentrates on the noise removal and signal to noise ratio (SNR) improvement of the motion-blurred star images, is proposed for the star sensor under dynamic conditions. A multi-seed-region growing technique with the kinematic recursive model for star image motion is given to find the star image regions and to remove the noise. Subsequently, a restoration strategy is employed in the extracted regions, taking the time consumption and SNR improvement into consideration simultaneously. Simulation results indicate that the region-confined restoration method is effective in removing noise and improving the centroiding accuracy. The identification rate and the average number of identified stars in the experiments verify the advantages of the region-confined restoration method.

Motion-blurred star acquisition method of the star tracker under high dynamic conditions

The star tracker is one of the most promising attitude measurement devices used in spacecraft due to its extremely high accuracy. However, high dynamic performance is still one of its constraints. Smearing appears, making it more difficult to distinguish the energy dispersive star point from the noise. An effective star acquisition approach for motion-blurred star image is proposed in this work. The correlation filter and mathematical morphology algorithm is combined to enhance the signal energy and evaluate slowly varying background noise. The star point can be separated from most types of noise in this manner, making extraction and recognition easier. Partial image differentiation is then utilized to obtain the motion parameters from only one image of the star tracker based on the above process. Considering the motion model, the reference window is adopted to perform centroid determination. Star acquisition results of real on-orbit star images and laboratory validation experiments demonstrate that the method described in this work is effective and the dynamic performance of the star tracker could be improved along with more identified stars and guaranteed position accuracy of the star point.