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Autofocus by Bayes Spectral Entropy Applied to Optical Microscopy

Published online by Cambridge University Press:  13 January 2016

Steffen Podlech
Steffen Podlech*
Affiliation:
Rybners HTX, Sp. Møllevej 72, Esbjerg 6700, Denmark
*
*Corresponding author.[email protected]
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Abstract

This study introduces a passive autofocus method based on image analysis calculating the Bayes spectral entropy (BSE). The method is applied to optical microscopy and together with the specific construction of the opto-mechanical unit, it allows the analysis of large samples with complicated surfaces without subsampling. This paper will provide a short overview of the relevant theory of calculating the normalized discrete cosine transform when analyzing obtained images, in order to find the BSE measure. Furthermore, it will be shown that the BSE measure is a strong indicator, helping to determine the focal position of the optical microscope. To demonstrate the strength and robustness of the microscope system, tests have been performed using a 1951 USAF test pattern resolution chart determining the in focus position of the microscope. Finally, this method and the optical microscope system is applied to analyze an optical grating (100 lines/mm) demonstrating the detection of the focal position. The paper concludes with an outlook of potential applications of the presented system within quality control and surface analysis.

Keywords

optical microscopyBayes spectral entropyimage analysisautofocuscomputer vision
Type
Techniques, Software, and Equipment
Information
Microscopy and Microanalysis , Volume 22 , Issue 1 , February 2016 , pp. 199 - 207
Copyright
© Microscopy Society of America 2016 

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References

Geusebroek, J.-M., Cornelissen, F., Smeulders, A.W.M. & Geerts, H. (2000). Robust autofocussing in microscopy. Cytometry 39, 1–9.3.0.CO;2-J>CrossRefGoogle Scholar
Gonzalez, R.C. & Woods, R.E. (2008). Digital Image Processing. Upper Saddle River, NJ, USA: Pearson Prentice Hall.Google Scholar
Hotchkiss, R.N., Washer, F.E. & Rosberry, F.W. (1951). Spurious resolution of photographic lenses. J Acoust Soc Am 41, 600–603.Google Scholar
Jeffreys, S.H. & Swirles, B. (1966). Methods of Mathematical Physics. Cambridge, UK: Cambridge University Press.Google Scholar
Kristan, M., Perš, J., Perše, M. & Kovačič, S. (2006). Bayes-spectral entropy-based measure of camera focus using a discrete cosine transform. Pattern Recognit Lett 27, 1431–1439.CrossRefGoogle Scholar
Neumann, B., Dämon, A., Hogenkamp, D., Beckmann, E. & Kollmann, J. (1989). A laser-autofocus for automatic microscopy and metrology. Sensors Actuators 17, 267–272.CrossRefGoogle Scholar
Santoz, A., Ortiz de Solórzano, C., Vaquero, J.J., Peña, J. & Malpica, N. (1997). Evaluation of autofocus functions in molecular cytogenetic analysis. J Microsc 188, 264–272.CrossRefGoogle Scholar
Stewart, J.E. (1965). Spurious spectral resolution and the spectral transfer function of monochromators. Appl Opt 4(5), 609–612.CrossRefGoogle Scholar
Vollath, D. (1988). Automatic focussing by correlative methods. J Microsc 147, 279–288.CrossRefGoogle Scholar
Xu, X., Wang, Y., Tang, J., Zhang, X. & Liu, X. (2011). Robust automatic focus algorithm for low contrast images using a new contrast measure. Sensors 11, 8281–8294.CrossRefGoogle ScholarPubMed