Minimum variance unbiased subpixel centroid estimation of point image limited by photon shot noise.

An unbiased subpixel centroid estimation algorithm of point image is proposed through the compensation of the systematic error of the center of mass method. The Cramér-Rao lower bound on centroid estimation variances is derived under the photon shot noise condition and is utilized to evaluate the proposed algorithm. Numerical analysis shows that the proposed centroid estimator attains the required lower bound; thus the proposed algorithm can be asserted as a minimum variance estimator. Simulation results indicate that the centroid accuracy is maximized when the Gaussian width of the signal spot is 0.2-0.3 pixel and the estimator can attain subpixel accuracy close to 1/100 pixel when 1000 photons are detected.

[1]  Richard Hornsey,et al.  Determining star-image location: A new sub-pixel interpolation technique to process image centroids , 2007, Comput. Phys. Commun..

[2]  Keigo Hirakawa.  Cross-talk explained , 2008, 2008 15th IEEE International Conference on Image Processing.

[3]  C. Liebe.  Star trackers for attitude determination , 1995 .

[4]  C. Rao,et al.  Accuracy analysis on centroid estimation algorithm limited by photon noise for point object , 2009 .

[5]  Brian F. Alexander,et al.  Elimination of systematic error in subpixel accuracy centroid estimation [also Letter 34(11)3347-3348(Nov1995)] , 1991 .

[6]  Roelof W. H. van Bezooijen.  SIRTF autonomous star tracker , 2003 .

[7]  C. Rao,et al.  Accuracy analysis of centroid calculated by a modified center detection algorithm for Shack–Hartmann wavefront sensor , 2008 .

[8]  R. H. Stanton,et al.  Acquisition and track algorithms for the Astros star tracker , 1985 .

[9]  Antoinette O'Grady.  A comparison of EMCCD, CCD and emerging technologies optimized for low light spectroscopy applications. , 2006, SPIE BiOS.

[10]  S. B. Grossman,et al.  Performance Analysis And Size Optimization Of Focal Planes For Point-Source Tracking Algorithm Applications , 1984 .

[11]  J Alexander Liddle,et al.  Fast, bias-free algorithm for tracking single particles with variable size and shape. , 2008, Optics express.

[12]  Kim A. Winick,et al.  Cramér–Rao lower bounds on the performance of charge-coupled-device optical position estimators , 1986 .

[13]  Carl Christian Liebe,et al.  Accuracy performance of star trackers - a tutorial , 2002 .

[14]  Shuji Nakamura,et al.  Suppression of sun interference in the star sensor baffling stray light by total internal reflection , 2005, SPIE Optical Systems Design.

[15]  B. J. Rye,et al.  Discrete spectral peak estimation in incoherent backscatter heterodyne lidar. I. Spectral accumulation and the Cramer-Rao lower bound , 1993, IEEE Trans. Geosci. Remote. Sens..

[16]  Gerard Rousset,et al.  Comparison of centroid computation algorithms in a Shack–Hartmann sensor , 2006 .

[17]  Cam Tropea,et al.  High-precision sub-pixel interpolation in particle image velocimetry image processing , 2005 .

[18]  Pierre Magnan,et al.  Pixel crosstalk and correlation with modulation transfer function of CMOS image sensor , 2005, IS&T/SPIE Electronic Imaging.

[19]  Steven E. Johnson,et al.  Bound on range precision for shot-noise limited ladar systems. , 2008, Applied optics.

[20]  Bedabrata Pain,et al.  CMOS active pixel sensor specific performance effects on star tracker/imager position accuracy , 2001, SPIE OPTO.

[21]  Robert B. Fisher,et al.  A Comparison of Algorithms for Subpixel Peak Detection , 1996 .