An Improved Star Detection Algorithm Using a Combination of Statistical and Morphological Image Processing Techniques

A star detection algorithm determines the position and magnitude of stars on an observed space scene. In this study, a robust star detection algorithm is presented that filters the noise out in astronomical images and accurately estimates the centroid of stars in a way that preserving their native circular shapes. The proposed algorithm suggests the usage of different filters including global and local filters as well as morphological operations. The global filter has been utilized to eliminate the blurring effect of the images due to system-induced noises with Point Spread Function (PSF) characteristics while the local filter aims to remove the noises with Gaussian distribution. The local filter should perform optimum noise reduction as well as not damaging the structure of the stars, therefore, a PCA (Principal Component Analysis) based denoising filter have been preferred to use. Although the PCA method is even good at preserving the mass integrity of stars, it may also have disruptive effects on the shape of them. Morphological operations help to restore this deformation. In order to verify the proposed algorithm, different types of noises having the Gaussian characteristics with different variance values have been inserted to astronomical star images to simulate the varied conditions of near space. Structural Similarity Index (SSIM) and Peak Signal to Noise Ratio (PSNR) parameters have been used as a performance metrics to show the accuracy of the filtering process. Furthermore, to demonstrate the overall accuracy of this method against to noise, the Mean Error of Centroid Estimation (MECE) has been achieved by means of the Monte Carlo analysis. Also, the performance of this algorithm has been compared with similar algorithms and the results show that this algorithm outperforms others.