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A Logistic Regression for Random Noise Removal in Image Deblurring

영상 디블러링에서의 임의 잡음 제거를 위한 로지스틱 회귀

  • Received : 2017.07.12
  • Accepted : 2017.09.28
  • Published : 2017.10.31

Abstract

In this paper, we propose a machine learning method for random noise removal in image deblurring. The proposed method uses a logistic regression to select reliable data to use them, and, at the same time, to exclude data, which seem to be corrupted by random noise, in the deblurring process. The proposed method uses commonly available images as training data. Simulation results show an improved performance of the proposed method, as compared with the median filtering based reliable data selection method.

Keywords

References

  1. R. Gonzalez and R. Woods, Digital Image Processing, Prentice-Hall, Englewood Cliffs, NewJergy, 2002.
  2. A. Tekalp and M. Sezan, "Quantitative Analysis of Artifacts in Linear Space-invariant Image Restoration," Multidimensional Systems and Signal Processing, Vol. 1, Issue 2, pp. 143-177, 1990. https://doi.org/10.1007/BF01816547
  3. N.Y. Lee and B. Lucier, "Preconditioned Conjugate Gradient Method for Boundary Artifacts-Free Image Deblurring," Inverse Problems and Imaging, Vol. 10, No. 1, pp. 195-225, 2016. https://doi.org/10.3934/ipi.2016.10.195
  4. D. Calvetti, and E. Somersalo, "Bayesian Image Deblurring and Boundary Effects," Proceeding of Society of Photo-Optical Instrumentation Engineers Conference Series, Vol. 5910, pp. 281-289, 2005.
  5. D. Calvetti, J. Kaipio, and E. Someralo, "Aristotelian Prior Boundary Conditions," International Journal of Mathematics and Computer Science, Vol. 1, pp. 63-81, 2006.
  6. N.Y. Lee, "Suppression of Defective Data Artifacts for Deblurring Images Corrupted by Random Valued Noise," Journal of Computational Mathematics, Vol. 33, No. 3, pp. 263- 282, 2015. https://doi.org/10.4208/jcm.1411-m4405
  7. J.Y. Lee and N.Y. Lee, "Cause Analysis and Removal of Boundary Artifacts in Image Deconvolution," Journal of Korea Multimedia Society, Vol. 17, No. 7, pp. 838-848, 2014. https://doi.org/10.9717/kmms.2014.17.7.838
  8. H.W. Engl, M. Hanke, and A. Neubauer, Regularization of Inverse Problems, Kluwer Academic Publishers, Dordrecht, 2000.
  9. R. Rudin and S. Osher, "Total Variation Based Image Restoration with Free Local Constraints," Proceeding of IEEE International Conference Image Processing, ICIP-94, Vol. 1, pp. 31-34, 1994.
  10. M.R. Hestenes and E. Stiefel, "Methods of Conjugate Gradients for Solving Linear Systems," Journal of Research of the National Bureau of Standards, Vol. 49, No. 6, pp. 409-436, 1952. https://doi.org/10.6028/jres.049.044
  11. C.M. Bishop, Pattern Recognition and Machine Learning, Springer, 2006.
  12. D.G. Luenberger and Y. Ye, Linear and Nonlinear Programming, Springer, 2008.