|

Defocused and blurred image recovery techniques

Authors: Konstandoglo A.V.
Published in issue: #5(46)/2020
DOI: 10.18698/2541-8009-2020-5-612


Category: Informatics, Computer Engineering and Control | Chapter: System Analysis, Control, and Information Processing, Statistics

Keywords: image recovery, defocusing, motion blur, point scattering function, Wiener filter, Tikhonov regularization method, Lucy-Richardson deconvolution, blind deconvolution
Published: 30.05.2020

The paper presents a review and analysis of common methods for recovering distorted images. The methods of inverse filtration, Wiener, Tikhonov, Lucy - Richardson filtration, blind deconvolution are analyzed. A simulation was performed of the restoration of artificially uniformly blurred and noisy images in the MATLAB environment using the described algorithms. It is shown that the main difficulty is the search for distortion parameters. For special cases, there are methods for a quick assessment of these parameters, but in the general case this problem is nontrivial. An iterative pyramidal approach to finding distortion parameters is considered as a solution. Based on the results of the analysis, the main limitations of recovery methods are identified.


References

[1] Gonzalez R., Woods R. Digital image processing. Pearson Education, Inc, 2008. (Russ. ed.: Tsifrovaya obrabotka izobrazheniy. Moscow, Tekhnosfera Publ., 2012.)

[2] Dash R., Majhi B. Motion blur parameters estimation for image restoration. Optik, 2014, vol. 125, no. 5, pp. 1634–1640. DOI: https://doi.org/10.1016/j.ijleo.2013.09.026

[3] Liplyanin A.Yu., Khizhnyak A.V., Mikhnenok E.I., et al. Analysis of restoration methods for optical-electronic images lubricated at motion. Doklady BGUIR, 2018, no. 2(112), pp. 40–46 (in Russ.).

[4] Field D. What is the goal of sensory coding? Neural Computat., 1994, vol. 6, no. 4, pp. 559–601. DOI: https://doi.org/10.1162/neco.1994.6.4.559

[5] Gonzalez R., Woods R., Eddins S. Digital image processing using MATLAB. Pearson, 2004. (Russ. ed.: Tsifrovaya obrabotka izobrazheniy v srede MATLAB. Moscow, Tekhnosfera Publ., 2006.)

[6] Panfilova K., Umnyashkin S. Linear blur compensation in digital images using Lucy-Richardson method. IEEE EIConRusNW, 2015, pp. 163–167. DOI: https://doi.org/10.1109/EIConRusNW.2016.7448179

[7] Shempliner V.V. Restoring of defocused images by method of two-fold Fourier transform and ridge regression. Nauchno-tekhnicheskiy vestnik informatsionnykh tekhnologiy, mekhaniki i optiki [Scientific and Technical Journal of Information Technologies, Mechanics and Optics], 2008, vol. 8, no. 3, pp. 60–70 (in Russ.).

[8] Karnaukhov V.N., Mozerov M.G. A gradient recovery restoration of multispectral images corrupted bymotion blur and blur parameters estimation based on a multi-targetmatching model. Informatsionnye protsessy [Information Processes], 2016, vol. 16, no. 2, pp. 162–169 (in Russ.).

[9] Miskin J., MacKay D.J.C. Ensemble learning for blind image separation and deconvolution. 2000. In: Advances in independent component analysis. Springer, 2010, pp. 123–141.

[10] Fergus R., Singh B., Hertzmann A.T., et al. Removing camera shake from a single photograph. ACM TOG, 2006, vol. 25, no. 3, pp. 787–794. DOI: https://doi.org/10.1145/1141911.1141956