Формирование и обработка изображений электронной микроскопии
Нестеренко Д.В.

Аннотация:

В работе обсуждаются проблемы получения изображений в электронной микроскопии. Рассматриваются математические модели формирования изображения сканирующих и просвечивающих электронных микроскопов на основе оптической и контрастной передаточных функций соответственно. Кратко описываются методы коррекции размытия и устранения шума изображений, полученных с использованием электронной микроскопии.

Abstract:
In this review the problems of image acquisition by electron microscopy are discussed. The imaging models obtained by scanning and transmission electron microscopes based on the optical transfer function and contrast transfer functions are considered. The methods applying for blur correction and image denoising are briefly described.

Ключевые слова :
электронная микроскопия, аберрации, методы обработки изображений.

Key words:
aberration correction, electron microscopy, image processing methods.

Литература:

  1. Scanning Electron Microscopy and X-Ray Microanalysis (3rd ed.) / J.  Goldstein, D. Newbury, D. Joy, C. Lyman, P. Echlin, E. Lifshin, L. Sawyer and J. Michael. – New York, Boston, Dordrecht, London, Moscow: Kluwer Academic/Plenum Publishers, 2003. - 689 p.
  2. Cуворов, Э.В. Физические основы экспериментальных методов исследования реальной структуры кристаллов / Э.В. Cуворов. Черноголовка: Изд-во ИПХВ РАН, 1999. – 232 с.
  3. Electron Microscopy Principles and Techniques for Biologists (2rd ed.) / J.J. Bozzola and L.D. Russell. – Boston: Jones and Bartlett, 1998. – 670 p.
  4. Manual for the SUPRA (VP) and ULTRA Scanning Electron Microscopes. – Carl Zeiss SMT Ltd, Germany, 2005.
  5. Scanning microscopy for nanotechnology / W. Zhou and Z.L. Wang. – New York: Springer, 2006. – 522 p.
  6. Tan, Y.Y. A Study on Central Moments of the Histograms from Scanning Electron Microscope Charging Images / Y.Y. Tan, K.S. Sim, and C.P. Tso // Scanning. – 2007. – Vol. 29. – P. 211-218.
  7. Scherzer, O. Ueber einige Fehler von Elektronenlinsen / O. Scherzer // Z. Phys. - 1936. – Vol. 101. – P. 593–603.
  8. Gabor, D. A new microscope principle / D. Gabor // Nature. – 1948. - Vol. 161. – P. 777-778.
  9. Gabor, D. Microscopy by reconstructed wave-fronts / D. Gabor // Proc. R. Soc. Lond. A. - 1949. – Vol. 197. – P. 454–487.
  10. Hawkes, P.W. Electron image processing - a survey / P.W. Hawkes // Comput. Graph. Image Process. – 1978. – Vol. 8. – P. 406-446.
  11. Atomic resolution transmission electron microscopy. In Science of microscopy, vol. 1 (eds P.W. Hawkes and J.C.H. Spence) / A.I. Kirkland, S.L.-y. Chang and J.L. Hut­chison. - NY: Springer, 2008. - P. 3-64.
  12. Petersen, T.C. Quantitative TEM-based phase retieval of MgO nanocubes using the transport of intensity equation / T.C. Petersen, V.J. Keast and D.M. Paganin // Ultramicroscopy. – 2008. – Vol. 108. – P. 805-815.
  13. Hawkes, P.W. Aberration correction past and present / P.W. Hawkes // Phil. Trans. R. Soc. A. – 2009. – Vol. 367. – P. 3637-3664.
  14. Advanced Optical Imaging Theory / M. Gu. – Berlin, Heidelberg: Springer-Verlag, 2000. – 214 p.
  15. Sarder, P. Deconvolution methods for 3-d fluorescence microscopy images / P. Sarder, A. Nehorai // IEEE Signal Process. Mag. – 2006. – Vol. 23. – P. 32-45.
  16. McNally, J.G. Three-dimensional imaging by deconvolution microscopy / J.G. McNally, T. Karpova, J. Cooper and J.A. Conchello // Methods. – 1999. – Vol. 19. – Vol. 373-385.
  17. Bovik, A.C. The Effect of Median Filtering on Edge Estimation and Detection / A.C. Bovik, T.S. Huang and D.C. Munson // Pattern Analysis and Machine Intelligence, IEEE Transactions on Pattern Analysis and Machine Intelligence. – 1987. – Vol. 9, Issue 2. – P. 181-194.
  18. Sun, T. Center weighted median filters: Some properties and their applications in image processing / T. Sun, M. Gabbouj and Y. Neuvo // Signal Processing. – 1994. – Vol. 35(3). – P. 213-229.
  19. Perona, P. Scale-space and edge detection using anisotropic diffusion / P. Perona, J. Malik // IEEE Transactions on Pattern Analysis and Machine Intelligence. – 1990. – Vol. 12(7). – P. 629-639.
  20. Tomasi, C. Bilateral filtering for gray and color images / C. Tomasi, R. Manduchi // Proceedings of the IEEE International Conference on Computer Vision, January 1998. - P. 839-846.
  21. Barash, D. Fundamental relationship between bilateral filtering, adaptive smoothing, and the nonlinear diffusion equation / D. Barash // Pattern Analysis and Machine Intelligence, IEEE Transactions on Pattern Analysis and Machine Intelligence. – 2002. – Vol. 24(6). – P. 844-847.
  22. Buades, A. The staircasing effect in neighborhood filters and its solution / A. Buades, B. Coll and J. Morel // IEEE Transactions on Image Processing. – 2006. – Vol. 15(6). – P. 1499-1505.
  23. Pantelic, R.S. The discriminative bilateral filter: An enhanced denoising filter for electron microscopy data / R.S. Pantelic, R. Rothnagel, C.-Y. Huang, D. Muller, D. Woolford, M.J. Landsberg [et al.] // Journal of Structural Biology. – 2006. – Vol. 155(3). – P. 395-408.
  24. Buades, A. A Review of Image Denoising Algorithms, with a New One / A. Buades, B. Coll and J. Morel // Multiscale Modeling & Simulation. – 2005. – Vol. 4(2). – P. 490-530.
  25. Jiang, W. Applications of a bilateral denoising filter in biological electron microscopy / W. Jiang, M. Baker, Q. Wu, C. Bajaj and W. Chiu // Journal of Structural Biology. – 2003. - Vol. 144(1-2). – P. 114-122.
  26. Sheppard, A.P. Techniques for image enhancement and segmentation of tomographic images of porous materials / A.P. Sheppard, R.M. Sok and H. Averdunk // Conference on New Materials and Complexity, Canberra, Australia, 2003, Nov 03-07.
  27. Biggs, D. Accelerated Iterative Blind Deconvolution / D. Biggs. – PhD Thesis, 1998. – P. 143-144.
  28. Jang, K.E. Single channel blind image deconvolution from radially symmetric blur kernels / K.E. Jang and J.C. Ye // Opt. Express. – 2007. – Vol. 15(7). – P. 3791-3803.
  29. Kundur, D. A novel blind deconvolution scheme for image restoration using recursive filtering / D. Kundur and D. Hatzinakos // Signal Processing, IEEE Transactions on Pattern Analysis and Machine Intelligence. – 1998. – Vol. 46(2). – P. 375-390.
  30. Deconvolution and Blind Deconvolution in Astronomy. In Blind image deconvolution: theory and applications (eds K. Egiazarian and P. Campisi) / E. Pantin, J.-L. Starck, F. Murtagh. - CRC Press, 2007. - P. 277-317.
  31. Lucy, L. An iterative technique for the rectification of observed distributions / L. Lucy // The Astronomical Journal. – 1974. – Vol. 79(6). – P. 745-754.
  32. Richardson, W.H. Bayesian-Based Iterative Method of Image Restoration / W.H. Richardson // J. Opt. Soc. Am. – 1972. – Vol. 62(1). – P. 55-59.
  33. Dempster, A. Maximum likelihood from incomplete data via the EM algorithm / A. Dempster, N. Laird and D. Ru­bin // Journal of the Royal Statistical Society. Series B (Methodological). – 1977. – P. 1-38.
  34. Fish, D.A. Blind deconvolution by means of the Richardson-Lucy algorithm / D.A. Fish, A.M. Brinicombe and E.R. Pike // J. Opt. Soc. Am. – 1995. - Vol. 12(1).
  35. Tsumuraya, F. Iterative blind deconvolution method using Lucy's algorithm / F. Tsumuraya, N. Miura and N. Ba­ba // Astronomy and Astrophysics. – 1994. – Vol. 282(2).
  36. White, R.L. Image restoration using the damped Richardson-Lucy method / R.L. White // Instrumentation in Astronomy VIII, Kailua, Kona, HI, USA, 1994.
  37. Do, C. What is the expectation maximization algorithm? / C. Do, S. Batzoglou // Nat Biotech. – 2008. – Vol. 26(8). – P. 897-899.
  38. Caron, J.N. Noniterative blind data restoration by use of an extracted filter function / J.N. Caron, N.M. Namazi and C.J. Rollins // Appl. Opt. – 2002. – Vol. 41(32). – P. 6884-6889.
  39. Carasso, A.S. APEX method and real-time blind deconvolution of scanning electron microscope imagery / A.S. Carasso, D.S. Bright and A.E. Vladar // Optical Engineering. – 2002. – Vol. 41(10). – P. 2499-2514.
  40. Koren, N. Guides: Sharpness 19.08.2009. http://www.imatest.com/guides/image-quality/sharpness.
  41. Puetter, R.C. Digital Image Reconstruction: Deblurring and Denoising / R.C. Puetter, T.R. Gosnell and A. Yahil // Annual Review of Astronomy and Astrophysics. – 2005. – Vol. 43(1). – P. 139-194.
  42. Zuo, B. Perceptual ringing metric to evaluate the quality of images restored using blind deconvolution algorithms / B. Zuo, D. Ming and J. Tian // Optical Engineering. – 2009. – Vol. 48(3). – P. 037004-037004-9.
  43. Lin, W. Perceptual impact of edge sharpness in images / W. Lin, Y. Gai and A. Kassim // IEE Proc.-Vis. Image Signal Process. – 2006. – Vol. 153(2). – P. 215-223

References:

  1. Scanning Electron Microscopy and X-Ray Microanalysis (3rd ed.) / J. Goldstein, D. Newbury, D. Joy, C. Lyman, P. Echlin, E. Lifshin, L. Sawyer and J. Michael. – New York, Boston, Dordrecht, London, Moscow: Kluwer Academic/Plenum Publishers, 2003. - 689 p.
  2. Physical Foundations of experimental methods of crystal real structure research / E.V. Suvorov. – Chernogolovka: “IPHP RAS” Publisher, 1999. – 232 p. – (In Russian).
  3. Electron Microscopy Principles and Techniques for Biologists (2rd ed.) / J.J. Bozzola and L.D. Russell. – Boston: Jones and Bartlett, 1998. – 670 p.
  4. Manual for the SUPRA (VP) and ULTRA Scanning Electron Microscopes. – Carl Zeiss SMT Ltd, Germany, 2005.
  5. Scanning microscopy for nanotechnology / W. Zhou and Z.L. Wang. – New York: Springer, 2006. – 522 p.
  6. Tan, Y.Y. A Study on Central Moments of the Histograms from Scanning Electron Microscope Charging Images / Y.Y. Tan, K.S. Sim, and C.P. Tso // Scanning. – 2007. – Vol. 29. – P. 211-218.
  7. Scherzer, O. Ueber einige Fehler von Elektronenlinsen / O. Scherzer // Z. Phys. - 1936. – Vol. 101. – P. 593–603.
  8. Gabor, D. A new microscope principle / D. Gabor // Nature. – 1948. - Vol. 161. – P. 777-778.
  9. Gabor, D. Microscopy by reconstructed wave-fronts / D. Gabor // Proc. R. Soc. Lond. A. - 1949. – Vol. 197. – P. 454–487.
  10. Hawkes, P.W. Electron image processing - a survey / P.W. Hawkes // Comput. Graph. Image Process. – 1978. – Vol. 8. – P. 406-446.
  11. Atomic resolution transmission electron microscopy. In Science of microscopy, vol. 1 (eds P.W. Hawkes and J.C.H. Spence) / A.I. Kirkland, S.L.-y. Chang and J.L. Hut­chison. - NY: Springer, 2008. - P. 3-64.
  12. Petersen, T.C. Quantitative TEM-based phase retieval of MgO nanocubes using the transport of intensity equation / T.C. Petersen, V.J. Keast and D.M. Paganin // Ultramicroscopy. – 2008. – Vol. 108. – P. 805-815.
  13. Hawkes, P.W. Aberration correction past and present / P.W. Hawkes // Phil. Trans. R. Soc. A. – 2009. –Vol. 367. – P. 3637-3664.
  14. Advanced Optical Imaging Theory / M. Gu. – Berlin, Heidelberg: Springer-Verlag, 2000. – 214 p.
  15. Sarder, P. Deconvolution methods for 3-d fluorescence microscopy images / P. Sarder, A. Nehorai // IEEE Signal Process. Mag. – 2006. – Vol. 23. – P. 32-45.
  16. McNally, J.G. Three-dimensional imaging by deconvolution microscopy / J.G. McNally, T. Karpova, J. Cooper and J.A. Conchello // Methods. – 1999. – Vol. 19. – Vol. 373-385.
  17. Bovik, A.C. The Effect of Median Filtering on Edge Estimation and Detection / A.C. Bovik, T.S. Huang and D.C. Munson // Pattern Analysis and Machine Intelligence, IEEE Transactions on Pattern Analysis and Machine Intelligence. – 1987. – Vol. 9, Issue 2. – P. 181-194.
  18. Sun, T. Center weighted median filters: Some properties and their applications in image processing / T. Sun, M. Gabbouj and Y. Neuvo // Signal Processing. – 1994. – Vol. 35(3). – P. 213-229.
  19. Perona, P. Scale-space and edge detection using anisotropic diffusion / P. Perona, J. Malik // IEEE Transactions on Pattern Analysis and Machine Intelligence. – 1990. – Vol. 12(7). – P. 629-639.
  20. Tomasi, C. Bilateral filtering for gray and color images / C. Tomasi, R. Manduchi // Proceedings of the IEEE International Conference on Computer Vision, January 1998. - P. 839-846.
  21. Barash, D. Fundamental relationship between bilateral filtering, adaptive smoothing, and the nonlinear diffusion equation / D. Barash // Pattern Analysis and Machine Intelligence, IEEE Transactions on Pattern Analysis and Machine Intelligence. – 2002. – Vol. 24(6). – P. 844-847.
  22. Buades, A. The staircasing effect in neighborhood filters and its solution / A. Buades, B. Coll and J. Morel // IEEE Transactions on Image Processing. – 2006. – Vol. 15(6). – P. 1499-1505.
  23. Pantelic, R.S. The discriminative bilateral filter: An enhanced denoising filter for electron microscopy data / R.S. Pantelic, R. Rothnagel, C.-Y. Huang, D. Muller, D. Woolford, M.J. Landsberg [et al.] // Journal of Structural Biology. – 2006. – Vol. 155(3). – P. 395-408.
  24. Buades, A. A Review of Image Denoising Algorithms, with a New One / A. Buades, B. Coll and J. Morel // Multiscale Modeling & Simulation. – 2005. – Vol. 4(2). – P. 490-530.
  25. Jiang, W. Applications of a bilateral denoising filter in biological electron microscopy / W. Jiang, M. Baker, Q. Wu, C. Bajaj and W. Chiu // Journal of Structural Biology. – 2003. - Vol. 144(1-2). – P. 114-122.
  26. Sheppard, A.P. Techniques for image enhancement and segmentation of tomographic images of porous materials / A.P. Sheppard, R.M. Sok and H. Averdunk // Conference on New Materials and Complexity, Canberra, Australia, 2003, Nov 03-07.
  27. Biggs, D. Accelerated Iterative Blind Deconvolution. – PhD Thesis, 1998. – P. 143-144.
  28. Jang, K.E. Single channel blind image deconvolution from radially symmetric blur kernels / K.E. Jang and J.C. Ye // Opt. Express. – 2007. – Vol. 15(7). – P. 3791-3803.
  29. Kundur, D. A novel blind deconvolution scheme for image restoration using recursive filtering / D. Kundur and D. Hatzinakos // Signal Processing, IEEE Transactions on Pattern Analysis and Machine Intelligence. – 1998. – Vol. 46(2). – P. 375-390.
  30. Deconvolution and Blind Deconvolution in Astronomy. In Blind image deconvolution: theory and applications (eds K. Egiazarian and P. Campisi) / E. Pantin, J.-L. Starck, F. Murtagh. - CRC Press, 2007. - P. 277-317.
  31. Lucy, L. An iterative technique for the rectification of observed distributions / L. Lucy // The Astronomical Journal. – 1974. – Vol. 79(6). – P. 745-754.
  32. Richardson, W.H. Bayesian-Based Iterative Method of Image Restoration / W.H. Richardson // J. Opt. Soc. Am. – 1972. – Vol. 62(1). – P. 55-59.
  33. Dempster, A. Maximum likelihood from incomplete data via the EM algorithm / A. Dempster, N. Laird and D. Ru­bin // Journal of the Royal Statistical Society. Series B (Methodological). – 1977. – P. 1-38.
  34. Fish, D.A. Blind deconvolution by means of the Richardson-Lucy algorithm / D.A. Fish, A.M. Brinicombe and E.R. Pike // J. Opt. Soc. Am. – 1995. - Vol. 12(1).
  35. Tsumuraya, F. Iterative blind deconvolution method using Lucy's algorithm / F. Tsumuraya, N. Miura and N. Ba­ba // Astronomy and Astrophysics. – 1994. – Vol. 282(2).
  36. White, R.L. Image restoration using the damped Richardson-Lucy method / R.L. White // Instrumentation in Astronomy VIII, Kailua, Kona, HI, USA, 1994.
  37. Do, C. What is the expectation maximization algorithm? / C. Do, S. Batzoglou // Nat Biotech. – 2008. – Vol. 26(8). – P. 897-899.
  38. Caron, J.N. Noniterative blind data restoration by use of an extracted filter function / J.N. Caron, N.M. Namazi and C.J. Rollins // Appl. Opt. – 2002. – Vol. 41(32). – P. 6884-6889.
  39. Carasso, A.S. APEX method and real-time blind deconvolution of scanning electron microscope imagery / A.S. Carasso, D.S. Bright and A.E. Vladar // Optical Engineering. – 2002. – Vol. 41(10). – P. 2499-2514.
  40. Koren, N. Guides: Sharpness 19.08.2009. http://www.imatest.com/guides/image-quality/sharpness.
  41. Puetter, R.C. Digital Image Reconstruction: Deblurring and Denoising / R.C. Puetter, T.R. Gosnell and A. Yahil // Annual Review of Astronomy and Astrophysics. – 2005. – Vol. 43(1). – P. 139-194.
  42. Zuo, B. Perceptual ringing metric to evaluate the quality of images restored using blind deconvolution algorithms / B. Zuo, D. Ming and J. Tian // Optical Engineering. – 2009. – Vol. 48(3). – P. 037004-037004-9.
  43. Lin, W. Perceptual impact of edge sharpness in images / W. Lin, Y. Gai and A. Kassim // IEE Proc.-Vis. Image Signal Process. – 2006. – Vol. 153(2). – P. 215-223
© 2009, ИСОИ РАН
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