Motion parameters estimation based on improved radon transform for blurred star image

Motion parameters estimation is a crucial key to blur star restoration which is helpful to improve dynamic performance of star sensor. However, motion blur under high dynamic conditions results in signal-to-noise ratio (SNR) decreasing seriously which brings about difficulties in motion parameters estimation. In our paper, an improved Radon transform by introducing a combination of Z-function and double threshold mask is proposed overcoming the obstacles introduced by noise. The proposed algorithm is effectively in enhancing parallel stripes on Fourier spectrum and can reduce estimation error introduced by noise. Moreover, motion parameters can be obtained accurately even under conditions with SNR as low as 9.1629 dB by applying our algorithm. Finally, experiments on simulated images as well as real ones with different motion angles and motion lengths are carried out, testifying the effectiveness of the proposed method.