Abstract—A prototype of the block-software system that is able to solve the main problems of image restoration and additional problems related to analysis/diagnostics of images and generation of databases of synthesized test images is proposed. The system can be used in the regime of emulation of the process of image transformation including blurring, analysis, and restoration (which allows tuning and training of the system for a specific device) and in the regime of analysis and restoration of blurred images. A method for estimation of the blurring operator using the observed blurred image is proposed. A learning algorithm for recognition of typical linear distortion operators is used to determine the type of blurring operator.
Similar content being viewed by others
REFERENCES
L. Yaroslavsky, Digital Holography and Digital Image Processing: Principles, Methods, Algorithms (Springer Science & Business Media, 2013).
J. Biemond, R. L. Lagendijk, and R. M. Mersereau, “Iterative methods for image deblurring,” Proc. IEEE 78, 856–883 (1990).
M. Banham and A. Katsaggelos, “Digital image restoration,” IEEE Signal Proc. Mag. 14 (2), 24−41 (1997).
F. Sroubek and J. Flusser, “Multichannel blind iterative image restoration,” IEEE Trans. Image Process. 12, 1094–1106 (2003).
Y. Yitzhaky and N. S. Kopeikai, “Identification of blur parameters from motion blurred images,” Graph. Models & Image Process. 59, 310–320 (1997).
V. Kober and V. Karnaukhov, “Restoration of multispectral images degraded by non-uniform camera motion,” J. Commun. Technol. Electron. 60, 1366−1371 (2015).
A. Chakrabarti, T. Zickler, and W. T. Freeman, “Analyzing spatially-varying blur,” in Proc. IEEE Conf. on Computer Vision and Pattern Recognition 2010, (IEEE, New York, 2010), pp. 2512–2519.
A. Levin, Y. Weiss, F. Durand, and W. T. Freeman, “Understanding and evaluating blind deconvolution algorithms,” Proc. IEEE Conf. on Computer Vision and Pattern Recognition 2010, (IEEE, New York, 2009), pp. 1964–1971 (2009).
V. Karnaukhov and V. Kober, “A fast preview restoration algorithm for space-variant degraded images,” Proc. SPIE, 9971 Applications of Digital Image Processing XXXIX, 2016, pp. 99712W 7. https://doi.org/10.1117/12.2236812
V. Karnaukhov and V. Kober, “Analysis of Linear Distortion Characteristics in Problems of Restoration of Multispectral Images,” J. Commun. Technol. Electron. 62, 1464–1469 (2017). https://doi.org/10.1134/S1064226917120063
V. Karnaukhov and V. Kober, “Blind identification of linear degradation operators in the Fourier domain,” in Proc. SPIE’s 60 Annual Meeting; Conference: Applications of Digital Image Processing XXXVIII, San Diego, California, USA, Aug. 9–13, 2015 (SPIE, 2015), Vol. 9599, p. 95992I-7.
V. Karnaukhov and M. Mozerov, “Restoration of multispectral images by the gradient reconstruction method and estimation of the blur parameters on the basis of the multipurpose matching model,” J. Commun. Technol. Electron. 61, 1426–1431 (2016) https://doi.org/10.1134/S106422691612010X
V. Karnaukhov and M. Mozerov, “Motion blur estimation based on multitarget matching model,” Opt. Engineering 55, 100502 (2016) https://doi.org/10.1117/1.OE.55.10.100502
A. V. Oppenhaim, and J. S. Lim, “The importance of phase in signals,” Proc. IEEE 69, 529–541 (1981).
W. Pratt, Digital Image Processing (Wiley, New York, 1978; Mir, Moscow, 1982).
R. C. Gonzalez, and R. E. Woods, Digital Image Processing (Prentice Hall, Upper Saddle River, New Jersey, 2008; Tekhnosfera, Moscow, 2012).
O. P. Milyukova and P. A. Chochia, “Application of Metrical and Topological Image Characteristics for Distortion Diagnostics in the Signal Restoration Problem,” J. Commun. Technol. Electron. 63, 637–642 (2018).
Noll A. Michael, “Cepstrum pitch determination,” J. Acoust. Soc. Am. 41, 293–309 (1967).
I. S. Gonorovskii, Radio Circuits and Signals (Sovetskoe Radio, Moscow, 1986) [in Russian].
V. Karnaukhov and V. Kober, “A correlation-based algorithm for detecting linearly degraded objects using noisy training images,” Proc. SPIE 9971, Applications of Digital Image Processing XLI 10752, 1075220-1-8 (2018). https://doi.org/10.1117/12.2319765
FUNDING
This work was supported by the Russian Science Foundation (project no. 14-50-00150).
Author information
Authors and Affiliations
Corresponding author
Additional information
Translated by A. Chikishev
Rights and permissions
About this article
Cite this article
Karnaukhov, V.N., Kober, V.I., Mozerov, M.G. et al. Design of a Block-Software System for a Posteriori Analysis and Restoration of Multispectral Images. J. Commun. Technol. Electron. 64, 827–833 (2019). https://doi.org/10.1134/S1064226919080229
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1134/S1064226919080229