А.А. Noskov - Post-graduate Student, P.G. Demidov Yaroslavl State University E-mail: noskoff.andrey@gmail.com Е.А. Aminova - Post-graduate Student, P.G. Demidov Yaroslavl State University E-mail: lena@piclab.ru А.L. Priorov - Dr.Sc. (Eng.), Associate Professor, P.G. Demidov Yaroslavl State University E-mail: andcat@yandex.ru

Image merging is a process of obtaining one image from multiple. The resulting image carries more information about the photographed scene, than each of the originals. Such an image can be more useful when we deal with human or image processing system. Algorithms that performed this task are used in a wide applying in practical: computer vision, robotics, medicine, forensics, etc. In general, the problem of limited depth of field optical relieving device is solved. The article outlines the general provisions forming multi- focus images, shows the classification of existing algorithms. In addition, the image distortion process of the blurring formation outside the focal plane was examined. The authors propose an algorithm of forming multi-focus images based on cellular automata. The results of the algorithm implementation are described in this article.

 

1. Chun-Hung Shen, Homer H. Chen Robust focus measure for low-contrast images // Consumer Electronics. Jan 2006. ICCE \'06. Digest of technical Papers. P. 69-70.
2. Huafeng Li, Yi Chai, Hongpeng Yin, Guoquan Liu Multifocus image fusion and denoising scheme based on homogeneity similarity // Optics Communications 285. 2012. P. 91-100.
3. Subbarao M., Choi T., Nikzad A. Focusing techniques // Optical Eng. 32. 1993. P. 2824-2836.
4. Subbarao M., Tyan J.K. Selecting the optimal focus measure for autofocusing and depth-from-focus // IEEE Trans. Pattern Analysis and Machine Intelligence 20. 1998. P. 864 - 870.
5. Voronov S.V. Development and modeling of pseudo-gradient procedures image attachment via informative criteria // Ulyanovsk. 2014. P. 31.
6. Nikolov S.G., Lewis J.J., O-Callaghan R.J., Bull D.R., Canagarajah C.N. Hybrid fused displays: between pixel- and region based image fusion // Proceedings of 7th International Conference on Information Fusion. Stockholm. Sweden. June 2004. P. 1072-1079.
7. Malik A. S., Choi T.-S., Nisar H. Depth map and 3D imaging applications // Algorithms and Technologies. 2011. № 285.
8. Balakrishnan N., Read C.B., Vidakovic B. Encyclopedia of statistical sciences. New York: Wiley. V. 3. P. 1992-1996.
9. Huigang Z., Xiao B., Huaxin Z. Hierarchical remote sensing image analysis via graph laplacian energy // IEEE Geoscience and Remote Sensing Letters. V. 10. № 2. P. 396-400.
10. Naidu V.P.S., Raol J.R. Pixel-level image fusion using wavelets and principal component analysis a comparative analysis // Defense Science Journal. May 2008. V. 58. № 3. P. 338.
11. Toffoli T., Margolus N. Mashiny kletochnykh avtomatov. M: Mir. 1991.
12. Hoekstra A., J. Kroc P.S. Simulating complex systems by cellular automata. Springer. 2010.
13. Wolfram S. A new kind of science. Wolfram Media. 2002.
14. Tania Stathaki Image fusion: algorithms and applications. Academic Press. 2008.
15. Burt P., Adelson E. The laplacian pyramid as a compact image code // IEEE Transactions on Communications. 1983. P. 532-540.
16. Xydeas C., Petrovic V. Objective image fusion performance measure // Electronics Letters. 2000. № 36. P. 308-309.
17. Petrovic V., Xydeas C. Sensor noise effects on signal-level image fusion performance // Information Fusion. 2003. V. 4. P. 167-183.
18. Matrosov M.A. Metody postoroenija izobrazhenijj rasshirennojj glubiny rezkosti. Dis. - kand. nauk / MGU im. M.V. Lomonosova. 2009.
19. Wang Z., Bovik A.C. A universal image quality index // IEEE Signal Processing Letters. 2002. Mar. V. 9. № 3. P. 81-84.
20. Piella G. New quality measures for image fusion // Proceedings of the Seventh International Conference on Information Fusion / Ed. by P. Svensson, J. Schubert. V. I. Mountain View, CA: International Society of Information Fusion. 2004. Jun. P. 542-546.