방송공학회논문지 (Journal of Broadcast Engineering)

  • 제17권4호
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  • Pages.625-633
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  • 2012
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  • 1226-7953(pISSN)
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  • 2287-9137(eISSN)
한국방송∙미디어공학회 (The Korean Institute of Broadcast and Media Engineers)
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Extended Depth of Focus 알고리듬 파라메타 초기설정에 관한 연구

On the Study of Initializing Extended Depth of Focus Algorithm Parameters

  • 유경무 (호서대학교 전자공학과) ;
  • 주효남 (호서대학교 디지털디스플레이공학과) ;
  • 김준식 (호서대학교 전자공학과) ;
  • 박덕천 (호서대학교 디지털디스플레이공학과) ;
  • 최인호 (호서대학교 전자공학과)
  • 투고 : 2012.03.19
  • 심사 : 2012.07.23
  • 발행 : 2012.07.30
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초록

카메라로부터 획득한 여러 장의 영상에서 3차원 정보를 얻어내기 위한 Extended Depth of Focus(EDF) 알고리듬은 최근 많은 연구가 이루어지고 있다. 피사물체의 깊이정보에 따른 제한된 초점으로 인해 초점이 일부분 맞는 여러 장의 이미지를 가지고 EDF알고리듬은 각 영상들의 focus 영역에서 하나의 focused 영상과 depth영상을 취득한다. 대부분의 영상처리 알고리듬이 그렇듯, EDF 알고리듬에 사용되는 파라메타들의 초기설정에 따라 결과에 큰 영향을 준다. 본 논문에서는 EDF알고리듬을 적용하기 전 입력영상의 기반으로 pyramid, wavelet transform, complex wavelet transform을 사용하였으며 EDF알고리즘에서 사용되는 파라메타들의 설정에 따른 각 알고리즘의 성능을 분석하였다. 본 논문에서 제시한 파라메타들은 입력영상의 크기에 따른 down sampling의 단계, 영상의 기반 알고리듬의 영상 복원에 사용되는 최하위 레벨의 이미지에 대한 취득 형태, 연산에 쓰이는 window size의 크기이다. 우리는 실험을 통해 제시한 입력영상에 따라 각 파라메타들이 미치는 영향에 대해 분석하였고, 기존에 사용되었던 일반적인 파라메타 선정방식보다 최적화된 파라메타 선정방식을 통해 얻어진 결과영상이 3dB ~ 19dB정도 개선된 것을 확인하였다.

Extended Depth of Focus (EDF) algorithms for extracting three-dimensional (3D) information from a set of optical image slices are studied by many researches recently. Due to the limited depth of focus of the microscope, only a small portion of the image slices are in focus. Most of the EDF algorithms try to find the in-focus area to generate a single focused image and a 3D depth image. Inherent to most image processing algorithms, the EDF algorithms need parameters to be properly initialized to perform successfully. In this paper, we select three popular transform-based EDF algorithms which are each based on pyramid, wavelet transform, and complex wavelet transform, and study the performance of the algorithms according to the initialization of its parameters. The parameters we considered consist of the number of levels used in the transform, the selection of the lowest level image, the window size used in high frequency filter, the noise reduction method, etc. Through extended simulation, we find a good relationship between the initialization of the parameters and the properties of both the texture and 3D ground truth images. Typically, we find that a proper initialization of the parameters improve the algorithm performance 3dB ~ 19dB over a default initialization in recovering the 3D information.

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참고문헌

  1. R. Wiesendanger, Scanning probe microscopy and spectroscopy, cambridge University Press, 1994
  2. H. Schreiber and J. H. Bruning, Phase shifting interferometry, in Optical Shop Testing, Chap. 14, Wiley, New York, 2006
  3. G.X. Zhang, Y.M. Fan, X. Gao, S.G. Liu, Z.R. Qiu, C.Z. Jiang, X.F. Li, J. B. Guo, A confocal probe based on time difference measurement, CIRP Annals, vol. 53, no. 1, pp. 417-420, 2004 https://doi.org/10.1016/S0007-8506(07)60729-X
  4. J. C. Wyant, White light extended source shearing interferometer, Applied Optics, vol.13 No.1, pp. 200-202, 1974 https://doi.org/10.1364/AO.13.000200
  5. P. Gil, S. Lafuente, S. Maldonado and F. J. Acevedo, Distance estimation form image defocus for video surveillance systems, IEEE Electronics Letters, vol. 40, no. 17, pp. 1047-1049, 2004 https://doi.org/10.1049/el:20045249
  6. Seong-O Shim, Tae-Sun Choi, Fast shape from focus algorithm based on focus matching, Korean Institute of Information Technology, vol.6, no.4, pp. 100-107, 2008
  7. A. G. Valdecasas, D. Marshall, J. M. Becerra, J. J. Terrero, On the extended depth of focus algorithms for bright field microscopy, vol. 32, no. 6, pp. 559-569, 2001 https://doi.org/10.1016/S0968-4328(00)00061-5
  8. Franz Stephan Helmli and Stefan Scherer, Adaptive Shape from Focus with an Error Estimation in Light Microscopy, 2nd Int'1 Symposium on Image and Signal Processing and Analysis (ISPA01), pp.188-193, June 2001.
  9. Aamir Saeed Malik, Tae-Sun Choi, A Novel Algorithm for Estimation og Depth Map using Image Focus for 3D Shape Recovery in the Presence of Noise, Pattern Recognition, doi:10.1016/ j.patcog. 2007.12. 014, 2008
  10. A. S Malik and T. S. Choi, Consideration of Illumination effects and optimization of window size for accurate calculation of depth map for 3D shape recovery, Pattern Recognition, vol. 40, no.1, pp. 154-170, 2007 https://doi.org/10.1016/j.patcog.2006.05.032
  11. H. Li, B. S. Manjunath, S. K. Mitra, Multisensor image fusion using the wavelet transform, Graphical Models and Image Process., vol. 57, no. 3, pp. 235-245, 1995 https://doi.org/10.1006/gmip.1995.1022
  12. R. J. Pieper, A. Korpel, Image processing for extended depth of field, Appl. Opt., vol. 22, no. 10, pp.1449-1453. 1983 https://doi.org/10.1364/AO.22.001449
  13. N. Kingsbury, Image processing with complex wavelets, Phil. Trans. R. Soc. Lond. A, vol. 357, no. 1760, pp, 2543-2560, 1999 https://doi.org/10.1098/rsta.1999.0447
  14. P. J. Burt, R. J. Kolczynski, Enhanced image capture through fusion, 4th International Conference on Computer vision, Berlin Germany, pp. 173-182, May 1993
  15. K. R. Castleman, Digital Image processing, Englewood Cliffs, NJ.: Prentice Hall, 1996
  16. R. C. Gonzalez, R. E. Woods, Digital Image Processing, MA.: Addison-Wesley, 1992
  17. V. Tympel, New high-level image capture system for conventional microscopy, Proc. of SPIE, Newport Beach CA. USA., vol. 2707, pp. 529-536. 1996
  18. V. Tympel, Three dimensional animation with a conventional light microscopy, Proc. of SPIE, San Jose CA. USA., vol. 2984. pp. 190-198, 1997
  19. F. Aguet, D. Van De Ville and M. Unser, Model-Based 2.5-D deconvolution for extended depth of field in bright field microscopy, IEEE Trans. on Image Processing, vol. 17, no. 7, pp. 1144-1153, 2008. https://doi.org/10.1109/TIP.2008.924393