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Immediate solution of EM algorithm for non-blind image deconvolution

  • Kim, Seung-Gu (Department of Big Data Science, Sangji University)
  • Received : 2022.01.07
  • Accepted : 2022.03.10
  • Published : 2022.03.31

Abstract

Due to the uniquely slow convergence speed of the EM algorithm, it suffers form a lot of processing time until the desired deconvolution image is obtained when the image is large. To cope with the problem, in this paper, an immediate solution of the EM algorithm is provided under the Gaussian image model. It is derived by finding the recurrent formular of the EM algorithm and then substituting the results repeatedly. In this paper, two types of immediate soultion of image deconboution by EM algorithm are provided, and both methods have been shown to work well. It is expected that it free the processing time of image deconvolution because it no longer requires an iterative process. Based on this, we can find the statistical properties of the restored image at specific iterates. We demonstrate the effectiveness of the proposed method through a simple experiment, and discuss future concerns.

Keywords

References

  1. Fergus R, Singh B, Hertzmann A, Roweis ST, and Freeman WT (2006). Removing camera shake from a single photograph, ACM Trans. Graph. 25, 787-794. https://doi.org/10.1145/1141911.1141956
  2. Green PJ (1990), On the use of EM algorithm for penalized likelihood estimation, Journal of Royal Statistical Society B, 52, 443-452.
  3. Levin A, Weiss Y, Durand F, and Freeman WT (2011). Efficient marginal likelihood optimization in blind deconvolution, CVPR.
  4. Lucy L (1974). Bayesian-based iterative method of image restorationJournal of Astronomy, 79, 745-754. https://doi.org/10.1086/111605
  5. Jin M, Roth S, and Favaro P (2018). Normalized blind deconvolution, Computer Vision - ECCV.
  6. Richardson W (1972). Bayesian-based iterative method of image restoration, Journal of the Optical Society of America A, 62, 55-59. https://doi.org/10.1364/JOSA.62.000055
  7. Searle SR (1982). Matrix Algebra Usefull for Statistics, Wiley.