Journal of Systems Engineering and Electronics ›› 2017, Vol. 28 ›› Issue (6): 1236-1247.doi: 10.21629/JSEE.2017.06.21

• SOFTWARE ALGORITHM AND SIMULATION • Previous Articles     Next Articles

Astronomical image restoration using variational Bayesian blind deconvolution#br#

Xiaoping Shi, Rui Guo*, Yi Zhu, and Zicai Wang   

  1. Control and Simulation Center, Harbin Institute of Technology, Harbin 150080, China
  • Online:2017-12-27 Published:2017-12-27


An algorithm is presented for image prior combinations based blind deconvolution and applied to astronomical images. Using a hierarchical Bayesian framework, the unknown original image and all required algorithmic parameters are estimated simultaneously. Through utilization of variational Bayesian analysis, approximations of the posterior distributions on each unknown are obtained by minimizing the Kullback-Leibler (KL) distance, thus providing uncertainties of the estimates during the restoration process. Experimental results on both synthetic images and real astronomical images demonstrate that the proposed approaches compare favorably to other state-of-the-art reconstruction methods.