Zhurnal Radioelektroniki - Journal of Radio Electronics. eISSN 1684-1719. 2020. No. 8

Full text in Russian (pdf)

Russian page


DOI https://doi.org/10.30898/1684-1719.2020.8.4

UDC 621.396


Estimation of spectral similarity of digital images


A. V. Kokoshkin

Fryazino Branch of Kotelnikov Institute of Radioengineering and Electronics of Russian Academy of Sciences, Vvedensky Sq.1, Fryazino Moscow region 141190, Russia

The paper is received on July 31, 2020


Abstract. This article proposes a new assessment of the quality of digital images - spectral similarity (Ssm - spectral similarity measure). Such an assessment can be used to determine the efficiency of a particular method for reconstructing digital images obtained in different wavelengths. This is illustrated by the example of filling gaps in real digital images. The interpolation method for the sequential calculation of the Fourier spectrum (IMSCS), cubic spline and neural network were tested. It has been established that, together with other objective criteria, spectral similarity can be used in the examination of various images or their fragments.†

Key words: quality of digital images, objective criteria, spectral similarity.


1.     Gonzalez Rafael C., Woods Richard E.  Digital Image Processing. Second Edition. Prentice-Hall, Inc. New Jersey. 2002.

2.     Monich Yu.I., Starovoitov V.V. Quality estimates for the analysis of digital images. Iskusstvennyy intelekt - Artificial Intelligence. 2008. No.4. P.376-386.  (In Russian)

3.     Pratt W.K. Digital Image Processing.† John Wiley and Sons, Inc., USA. 1978.

4.     Pirogov Yu.A., Gladun V.V., Tischenko D.A., Timanovsky A.L., Shlemin I.V., Dzhen S.F. Superresolution in the Millimeter Waves Radio-Vision Systems. Zhurnal radioelectroniki - Journal of Radio Electronics. 2004. No.3. Available at: http://jre.cplire.ru/jre/mar04/3/text.html (In Russian)

5.     Bates R.H., McDonnell M.J. Image Restoration and Reconstruction. (Oxford Engineering Science Series).† Oxford University Press. 1986. 320 p.

6.     Potapov A.A., editor. Noveyshiye metody obrabotki izobrazheniy [Latest methods of image processing]. Moscow, FIZMATLIT Publ. 2008. 496 p. (In Russian)

7.     Wang Z., Bovik A.C., Sheikh H.R Image quality assessment: From error visibility to structural similarity. IEEE transaction on Image Processing.† 2004.† Vol. 13. No.4. P.309.

8.     Wang X., Tian B., Liang C., Shi D. Blind Image Quality Assessment for Measuring Image Blur.† Proceedings of Congress on Image and Signal 2008.

9.     Zhuravel I.M. Kratkiy kurs teorii obrabotki izobrazheniy [A short course in the theory of image processing]. [online]. Available at: http://matlab.exponenta.ru/imageprocess/book2/index.php (In Russian)

10.  Avcibas I., Sankur B., Sayood K. Statistical evaluating of image quality measures. Journal of Electronic Imaging. 2002. Vol.11. No.2. .206-223.

11.    Makarov A.O. Algoritmy uvelicheniya prostranstvennogo razresheniya i obrabotki mul'tispektral'nykh sputnikovykh izobrazheniy [Algorithms for increasing spatial resolution and processing multispectral satellite images]. PhD thesis. Minsk. 2006. 156 p. (In Russian)

12.    Kokoshkin A.V., Korotkov V.A., Korotkov K.V., Novichikhin E.P.† A method for predicting a possible improvement in the quality of distorted images. Zhurnal radioelectroniki - Journal of Radio Electronics. 2015. No.6.† Available at: http://jre.cplire.ru/jre/jun15/5/text.html(In Russian)

13.    Kokoshkin A.V., Korotkov V.A., Korotkov K.V., Novichikhin E.P. Comparison of objective methods for assessing the quality of digital images. Zhurnal radioelectroniki - Journal of Radio Electronics, 2015. No.6. Available at: http://jre.cplire.ru/jre/jun15/15/text.html (In Russian)

14.    Wilder W.C. Subjective Relevant Error Criteria for Pictorial Data Processing. †Purdue University, School of Electrical Engineering, Report TR-EE 72-34, December 1972.

15.    Gulyaev Yu.V., Zrazhevsky A.Yu., Kokoshkin A.V., Korotkov V.A., Cherepenin V.A. Correction of the spatial spectrum distorted by the optical system using the reference image method. Part 3. Universal reference spectrum.† Zhurnal radioelectroniki - Journal of Radio Electronics. 2013. No.12. Available at:† http://jre.cplire.ru/jre/dec13/3/text.html (In Russian)

16.  Kokoshkin A.V., Korotkov V.A., Korotkov K.V., Novichikhin E.P. Using the Fourier spectrum of the image for retouching and restoring the missing parts of the image distorted by the hardware function. Zhurnal radioelectroniki - Journal of Radio Electronics. 2016. No.7. Available at: http://jre.cplire.ru/jre/jul16/4/text.html (In Russian)

17.  Ashkenazy A.V. Splayn-poverkhnosti. Osnovy teorii i vychislitel'nyye algoritmy [Spline surfaces. Fundamentals of theory and computational algorithms]. Tver. †Publishing house of Tver State. University. 2003.† 82 p. (In Russian)

18.  Nesterenko E.A. The ability to use spline surfaces to build surfaces based on survey results. Zapiski Gornogo instituta ‒ Notes of the Mining Institute. 2013. Vol.204. P.127 - 133. (In Russian)

19.  Movavi Photo Editor [online]. Available at: https://www.movavi.com/photo-editor/

20.  Kokoshkin A.V., Korotkov V.A., Korotkov K.V., Novichikhin E.P.†† Retouching and restoration of the absent parts† of images by means of the iterative calculation of their spectra. Kompyuternaya optika ‒ Computer Optics. 2019. Vol.43. No. 6. P.1030-1040. https://doi.org/10.18287/2412-6179-2019-43-6-1030-1040† (In Russian)


For citation:

Kokoshkin A.V. Estimation of spectral similarity of digital images. Zhurnal Radioelektroniki - Journal of Radio Electronics. 2020. No. 8. https://doi.org/10.30898/1684-1719.2020.8.4 (In Russian)