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 used in computational photography. Image fusion is a sub-field of image processing in which two or more images of a scene are combined into a single composite image that is more informative and is more suitable for visual perception and for digital processing. The depth of image can be restored via binocular (trinocular) systems in case absence of physical interaction with the captured scene as well as with a few shots taken at different settings of monocular system. Estimation the depth of sharpness on the field of image is a key problem in the computational photography in general and the main task of multi- focused images construction in particular. This problem arises at the time of transition from the three-dimensional perception of a two-dimensional projection of the image. Based on the results of the experiment, it is possible to make some conclusions, suggest the following recommendations on the use of image focus assessment methods. Algorithms that performed this task are used in a wide applying in practical: computer vision, robotics, medicine, forensics, etc. The most popular image focus assessment for usage in the task of forming a multi-focused image were considered. The classification of metrics evaluation was done. Experiments on suggested image focus metrics construction were minutely described. The forms of referenced characteristics were proposed. Using correlation analysis to select the best from the point of view of the problem, metrics and estimated time required for their computation were discussed.

 

1. Noskov A., Volokhov V., Aminova E. Multi-focus image fusion based on cellular automata method // Proceeding of the 17th conference of FRUCT association. Yaroslavl. Russia. 2015. P. 136-141.
2. Huang W., Jing Z. Evaluation of focus measures in multi-focus image fusion // Pattern Recognition Letters 28. 2007. P. 493-500.
3. Pohl C., Van Genderen J.L. Multisensor image fusion in remote sensing: Concepts, methods and applications // International Journal of Remote Sensing. V. 19. 1998. P. 823-854.
4. Huafeng Li, Yi Chai, Hongpeng Yin, Guoquan Liu Multifocus image fusion and denoising scheme based on homogeneity similarity // Optics Communications. V. 285. 2012. P. 91-100.
5. 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.
6. Petruz S., Puig D., Garcia M.A. Analysis of focus measure operators for shape-from-focus // Pattern Recognition. V. 46, № 5. May 2013. P. 1415-1432.
7. Shirvaikar M. An optimal measure for camera focus and exposure // Southeastern Symposium on System Theory. 2004. P. 472-475.
8. Sobel I., Feldman G. A 3x3 isotropic gradient operator for image processing // Stanford Artificial Project. 1968.
9. Subbarao M., Choi N., Nikzad A. Focusing techniques // Journal of Optical Engineering 32. 1993. P. 2824-2836.
10. Lawrence G. Roberts Machine perception of three-dimensional solids // Massachusetts Institued of Technology. 22 May 1963.
11. Malik A.S., Choi T.S. A novel algorithm for estimation of depth map using image focus for 3D shape recovery in the presence of noise // Pattern Recognition 41. 2008. P. 2200-2225.
12. Prewitt J.M.S. Object Enhancement and Extraction // Picture processing and Psychopictorics, Academic Press. 1970.
13. Chern N.K., Neow P.A., Ang M.H. Practical issues in pixel-based autofocusing for machine vision // Proceedings of the International Conference on Robotics and Automation. V. 3. 2001. P. 2791-2796.
14. Shen C.H., Chen H.H. Robust focus measure for low-contrast images // Digest of Technical Papers of International Conference on Consumer Electronics. 2006. P. 69-70.
15. Wee C., Paramesran R. Measure of image sharpness using eigenvalues // Information Sciences 177. 2007. P. 2533-2552.
16. Xie H., Rong W., Sun L. Wavelet-based focus measure and 3-d surface reconstruction method for microscopy images // RSJ International Conference on Intelligent Robots and Systems. 2006. P. 229-234.
17. Kekre H.B., Athawale A., Sadavarti D. Algorithm to generate Kekres wavelet transform from Kekres transform // International Journal of Engineering Science and Technology. V. 2(5). 2010. P. 756-767.
18. De I., Chanda B. A simple and efficient algorithm for multifocus image fusion using morphological wavelets // Signal Processing, V. 86. 2006. P. 924-936.
19. Noskov A.A., Aminova E.A., Volokhov V.A. Sravnenie metrik ocenki sfokusirovannosti dlja zadachi formirovanija polnostju sfokusirovannykh izobrazhenijj // Dokl. 18-jj Mezhdunar. konf. «Cifrovaja obrabotka signalov i ee primenenie (DSPA-2016)». Moskva. 2016. T. 2. S. 729-734.
20. Stathaki T. Image Fusion: Algorithms and applications. Academic Press. 2008.