Enhancing the Informativeness of Multispectral Images by means of Multimodal Image Fusion


  • A. P. Hryvachevskyi Lviv Polytechnic National University, Ukraine
  • I. N. Prudyus Lviv Polytechnic National University, Ukraine




image fusion, multispectral monitoring, image informativeness, wavelet transform, regression analysis


The research is devoted to the problem of combining graphic information from sensors of various physical nature in multispectral monitoring systems using methods of multimodal image fusion. Each of the sensors of the multispectral monitoring system allows to obtain digital images of the observed scene in different ranges of electromagnetic radiation. In this paper, we consider a two-spectral monitoring system, the frst sensor of which operates in the visible range, and the second in the infrared. The main problem with multimodal image fusion is that each partial sensor of the multispectral monitoring system represents specifc characteristics of the environment (brightness, thermal or radar contrasts of objects, etc). Another, no less important problem is the different spatial resolution of sensors of different physical nature and the inconsistency of their felds of view. Therefore, the problem of effective multimodal image fusion is not trivial. As a criterion for the effectiveness of combining graphical information in a single fused image, which should contain the maximum available useful information from various sensors, the informativeness of this image is chosen. A quantitative assessment of image informativeness is proposed to be performed using an improved method based on multicriteria analysis of image parameters. Multimodal image fusion is performed using the proposed method based on the discrete wavelet transform with the formation of low-frequency wavelet coefcients of the resulting wavelet spectrum by analyzing the regression communication model between the input images from different sensors. Confrmed experimentally that the proposed method of image fusion makes it possible to synthesize more informative multispectral images than known algorithms. The application of proposed method of image fusion for monitoring objects in difcult observation conditions (smoke, fog, low illumination) allows to increase the efciency of the multispectral monitoring system and signifcantly reduce the amount of redundant information coming to the operator of the system.

Author Biographies

A. P. Hryvachevskyi, Lviv Polytechnic National University

Hryvachevskyi A. P., Postgraduate Student

I. N. Prudyus, Lviv Polytechnic National University

Prudyus I. N., Doc. of Sci (Tech), Prof.


Zheng Y. (2011) Image Fusion and Its Applications. InTech, 252 p. DOI: 10.5772/691

Frolov V.N., Tupikov V.A., Pavlova V.A. and Aleksandrov V.A. (2016) Informational image fusion methods in multichannel optoelectronic systems. Izvestiya Tul'skogo gosudarstvennogo universiteta. Tekhnicheskie nauki, No. 11-3, pp. 95-104.

Zheng Y., Essock E.A. and Hansen B.C. (2004) An advanced image fusion algorithm based on wavelet transform: incorporation with PCA and morphological processing. Image Processing: Algorithms and Systems III. DOI: 10.1117/12.523966

Hryvachevskyi A. P. (2015) Analysis of the methods of signal data fusion of partial spectral channels in the monitoring systems of objects and scenes. Bulletin of the Lviv Polytechnic National University. Radioelectronics and Telecommunications, No. 818, pp. 55-61.

Kondratov P., Ohanesyan A., Tkachenko V., Pradyus I., Lazko L. and Hryvachevskyi A. (2016) Detection and allotment of the objects based on multispectral monitoring. 2016 13th International Conference on Modern Problems of Radio Engineering, Telecommunications and Computer Science (TCSET). DOI: 10.1109/tcset.2016.7452030

Zubkov A. M., Prudyus I. N., Diakonov A. V., Martynenko S. A., Mymrikov D. O. and Sherba A. A. (2011) Multispectral detector of ground objects, Pat. UA94566.

Bondarenko A. V. and Bondarenko M. A. (2017) Apparatno-programmnaya realizatsiya mul'tispektral'noi sistemy uluchshennogo videniya [Hardware-software implementation of the multispectral system of improved vision]. Sovremennaya elektronnika, No. 1, pp. 32-37.

Hryvachevskyi A., Fabirovskyy S., Lazko L. and Tkachenko V. (2017) The influence of destabilizing factors in the high resolution multispectral imaging systems. 2017 International Conference on Information and Telecommunication Technologies and Radio Electronics (UkrMiCo). DOI: 10.1109/ukrmico.2017.8095371

Hryvachevskyi A. P. and Fabirovskyy S. E. (2017) Matching up of images which formed by sensors of different physical nature in the process of signal fusion in multispectral monitoring systems. Bulletin of the Lviv Polytechnic National University. Radioelectronics and Telecommunications, No. 874, pp. 73-80.

Sahoo P.K. and Pati U.C. (2015) Image registration using mutual information with correlation for medical image. 2015 Global Conference on Communication Technologies (GCCT). DOI: 10.1109/gcct.2015.7342619

Saidi F., Chen J. and Wang P. (2016) A refined automatic co-registration method for high-resolution optical and sar images by maximizing mutual information. 2016 IEEE International Conference on Signal and Image Processing (ICSIP). DOI: 10.1109/siprocess.2016.7888258

Wang Z. and Bovik A. (2002) A universal image quality index. IEEE Signal Processing Letters, Vol. 9, Iss. 3, pp. 81-84. DOI: 10.1109/97.995823

Wang Z., Bovik A., Sheikh H. and Simoncelli E. (2004) Image Quality Assessment: From Error Visibility to Structural Similarity. IEEE Transactions on Image Processing, Vol. 13, Iss. 4, pp. 600-612. DOI: 10.1109/tip.2003.819861

Bondarenko M. A. and Drynkin V. N. (2016) Assessment of the information content of image fusion in multispectral vision systems Software systems and computational methods), No. 1, pp. 64-79. DOI: 10.7256/2305-6061.2016.1.18047

Bogdanov A. P. and Romanov Yu. N. (2012) Digital images quality assessment. Mekhanika, upravlenie i informatika), No 9, pp. 218-226.

Krivosheev M. and Fedunin V. (2007) International standards for digital television broadcasting Elektronika: Nauka, Tekhnologiya, Biznes, No 8, pp. 28-36.

Seltman H. J. (2015) Experimental Design and Analysis), 227 p.





Computing methods in radio electronics