The method for visualization of details on the image of the still fracture surface of impact testing specimen on the base of the series of optical microscope images with the small focus depth is proposed. A series of images of a still object was obtained by successive change of the distance to focal plane with a certain fixed step. The local contrast maximum principle was applied to images in the sequence for construction of aggregated all-in-focus image. A level map was formed from the indexes of the image in the series, which at certain pixel indicates the most probable distance to the corresponding point of the object. The Chebyshev best approximation was used for estimation of the height of the relief to provide better 3-D visualization of a three-dimensional image of the fracture surface. The area of fracture surface was estimated by train-gulation of alpha shape.

1. Kosarevych, R. Ya.; Student, O. Z.; Svirs'ka, L. M.; Rusyn, B. P.; Nykyforchyn, H. M. Computer analysis of characteristic elements of fractographic images. Materials Science, 2013, 48, 4, 474-481. https://doi.org/10.1007/s11003-013-9527-0

2. Hredil, M.; Krechkovska, H.; Student, O.; Kurnat, I. Fractographic features of long term operated gas pipeline steels fracture under impact loading. Procedia Structural Integrity, 2019, 21, 166-172. https://doi.org/10.1016/j.prostr.2019.12.098

3. Student, O. Z.; Krechkovs'ka, H. V.; Nykyforchyn, H. M.; Kurnat, I. M. Fractographic criterion of attainment of the critical technical state by carbon steels. Materials Science, 2019, 55, 2, 160-167. https://doi.org/10.1007/s11003-019-00283-2

4. Krechkovska, H.; Hredil, M.; Student, O. Structural and fractographic features of gas pipeline steel degradation. Degradation assessment and failure prevention of pipeline systems Eds. Gabriella Bolzon, Giovanna Gabetta, Hryhoriy Nykyforchyn. Springer: Nature Switzerland AG, 2020, 45-60. https://doi.org/10.1007/978-3-030-58073-5_4

5. Niederoest, J.; Niederoest, M.; Scucka, J. Shape from focus: fully automated 3d reconstruction and visualization of microscopic objects. Proceedings of 6th International Conference on Optical 3-D Measurement Techniques, 2003, 4-11. 6. Lesiuk, G.; Correia, J.A.F.O.; Krechkovska, H.V.; Pekalski, G.; Jesus, A.M.P.; Student, O. Degradation Theory of Long Term Operated Materials and Structures. In: Structural Integrity, 2021, 15, 170 p.

7. Nykyforchyn, H.; Tsyrulnyk, O.; Zvirko O. Electrochemical fracture analysis of in-service natural gas pipeline steels. Procedia Structural Integrity, 2018, 13, 1215-1220. https://doi.org/10.1016/j.prostr.2018.12.250

8. Averkin, A.; Potapov, A. S.; Rozhkov, A. 3D reconstruction and visualization of microscopic images from multifocus image sequence Scientific and Technical Journal of Information Technologies, Mechanics and Optics. 2011, 6 (76), 12-16.

9. Gaganov, V.; Ignatenko, A. Robust shape from focus via Markov random 575 fields, in: Proceedings of GraphiCon'2009, 2009, 74-80.

10. Aydin, T.; Akgul, Y. A new adaptive focus measure for shape from focus. in British Machine Vision Conference 2008, 8.1-8.10. https://doi.org/10.5244/C.22.8

11. Surh, J.; Jeon, H.-G.; Park, Y.; Im, S.; Ha, H.; Kweon, I. S. Noise Robust Depth from Focus Using a Ring Difference Filter. In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR); IEEE, 2017 - 2017; pp 2444-2453. https://doi.org/10.1109/CVPR.2017.262

12. Nayar, S. K. Shape from focus system. In Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition; IEEE Comput. Soc. Press, 1992; pp 302-308.

13. Zhang, X.; Liu, Z.; Jiang, M.; Chang, M. Fast and accurate auto-focusing algorithm based on the combination of depth from focus and improved depth from defocus. Optics express 2014, 22, 31237-31247. https://doi.org/10.1364/OE.22.031237

14. Pertuz, S.; Puig, D.; Garcia, M. A. Analysis of focus measure operators for shape-from-focus. Pattern Recognition 2013, 46, 1415-1432. https://doi.org/10.1016/j.patcog.2012.11.011

15. Pertuz, S.; Puig, D.; Garcia, M. A.; Fusiello, A. Generation of all-in-focus images by noise-robust selective fusion of limited depth-of-field images. IEEE transactions on image processing 2013, 22, 1242-1251. https://doi.org/10.1109/TIP.2012.2231087

16. Berehulyak, O. R.; Vorobel, R. A. Formation of steel fracture image by a series of multi-focus images using local contrast. In Proceedings of IV scientific and technical conference "Computational methods and systems of information transformation" Lviv: IPM NASU, 2016, 4, 209 - 212.

17. Vorobel, R.A.: Logarithmic Image Processing. Naukova Dumka. Kyiv (2012).

18. Best approximations of table functions: (Algorithms and programs) / Ed. B. A. Popov; Institute of Cybernetics of the Academy of Sciences of the Ukrainian SSR. Rep. fund of algorithms and programs. Kyiv, 1973, P. 212.

19. Edelsbrunner, H.; Mucke, E.P. Three-dimensional alpha-shapes. ACM Trans Graph. 1994, 13, 43-72. https://doi.org/10.1145/174462.156635