Automatic centroid extraction method for noisy star image

Star images obtained by star sensors have a low signal-to-noise ratio due to various physical constraints. Low resolution also causes stellar centroid extraction error when traditional methods such as the Gaussian filter or adaptive median filter are utilised to de-noise star images. An automatic centroid extraction method for noisy low-resolution star images is proposed in this study. First, sparse representation is utilised to de-noise the Poisson-Gaussian mixed noise of the low-resolution star image. A high-resolution star image is then reconstructed by using the low-resolution sparse coefficient. Finally, the stellar centroids are extracted automatically by learning the relationship between the high-resolution star image and corresponding stellar centroid image. Experimental results indicate that the positioning accuracy of the stellar centroids is also greatly enhanced by the reconstructed high-resolution stellar centroid image. The correct rate of stellar centroid recognition is 99.35%; the positioning accuracy of stellar centroid and computing time are 16.21 and 11.30ms, respectively. The probability distributions of Poisson and Gaussian noises are 0.50 and 0.08, respectively, while the proposed method correctly recognises stellar centroids at a rate of 76.56%. The results presented here may provide a workable foundation for accurate attitude calculations of the celestial navigation system.