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

机译:基于高动态和长曝光条件下多帧叠加的运动模糊的星形图像恢复

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Under high dynamic and long exposure conditions, the number of recognized stars on motion-blurred star images decreases, thereby degrading the attitude accuracy of star sensors. To improve the attitude accuracy, a restoration method based on multi-frame superposition, which focuses on the noise removal and quality of restored star images, is proposed for a star sensor. During each short exposure time, the corrected coordinate variation of the same star spot between adjacent star images is determined using a motion recursive model. Subsequently, the corrected star spot region is obtained, and the noise is removed. A restoration algorithm based on multi-frame superposition is proposed, taking the time consumption and quality of restored star image considered simultaneously. Simulation results indicate that the proposed restoration method based on multi-frame superposition is effective in removing noise and improving the quality of restored star images. The star recognition rate in simulation experiments verifies the advantages of the proposed method.
机译:在高动态和长时间的曝光条件下,运动模糊星图像上的识别恒星的数量降低,从而降低了星传感器的姿态精度。为了提高姿态精度,提出了一种基于多帧叠加的恢复方法,该方法专注于恢复星形图像的噪声去除和质量,是为了一个星传感器。在每个短曝光时间期间,使用运动递归模型确定相邻星图像之间的相同星光点的校正坐标变化。随后,获得校正的星光点区域,并去除噪声。提出了一种基于多帧叠加的恢复算法,同时考虑的恢复星形图像的时间消耗和质量。仿真结果表明,基于多帧叠加的建议恢复方法是有效的去除噪声,提高恢复星图像的质量。仿真实验中的星识别率验证了所提出的方法的优点。

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