APPLICATION OF HISTOGRAM OUTLIER ANALYSIS ON THE IMAGE DEGRADATION MODEL FOR BEST FOCAL POINT SELECTION

  • Shin, Hyun-Kyung (Department of Mathematics & Information, Kyungwon University)
  • Published : 2009.01.31

Abstract

Microscopic imaging system often requires the algorithm to adjust location of camera lenses automatically in machine level. An effort to detect the best focal point is naturally interpreted as a mathematical inverse problem [1]. Following Wiener's point of view [2], we interpret the focus level of images as the quantified factor appeared in image degradation model: g = $f{\ast}H+{\eta}$, a standard mathematical model for understanding signal or image degradation process [3]. In this paper we propose a simple, very fast and robust method to compare the degradation parameters among the multiple images given by introducing outlier analysis of histogram.

Keywords

References

  1. Tarantola, A., Inverse Problem Theory and Methods for Model Parameter Estimation, SIAM, Philadelphia, PA, 2005.
  2. Wiener, N., Cybernetics or Control and Communication in the Animal and the Machine, The MIT Press, Cambridge, MA, 1961.
  3. Gonzalez, R.C., and Woods, R.E., Digital Image Processing, Prentice Hall, Upper Saddle River, NJ, 2002.
  4. Brunelli, B., and Mich, O., Histograms Analysis for Image Retrieval, ITC-irst, Interactive Sensory Systems Division, I-38050 Povo, Trento, Italy, Feb., 2000.
  5. Hao, P., Zhang, C., and Dang, A., Co-Histogram and Image Degradation Evaluation, International Conference on Image Analysis and Recognition (ICIAR), pp. 195–203, Sep., 2004.
  6. Hao, P., and Chen, Y., Co-Histogram and Its Application in Video Analysis, In Proceed ings of IEEE International Conference on Multimedia and Expo (ICME), Taiwan, 2004.
  7. Taubman, D.S., Marcellin, M.W., JPEG2000: standard for interactive imaging, Proceedings of the IEEE, Vol. 90, No. 8, pp. 1336-1357, 2002. https://doi.org/10.1109/JPROC.2002.800725
  8. Datcu, M., Schwarz, G., Schmidt, K., and Reck, C., Histogram analysis of JPEG com pressed images as an aid in image deblocking, DCC'95: Proceedings of the Conference on Data Compression, IEEE Computer Society, pp. 425, Washington, DC, 1995.
  9. Kalyanpur, A., Neklesa, V.P., Taylor, C.R., Daftary, A.R., and Brink, J.A., Evaluation of JPEG and Wavelet Compression of Body CT Images for Direct Digital Teleradiologic Transmission, Radiology, v. 217, pp. 772–779, 2000. https://doi.org/10.1148/radiology.217.3.r00nv22772
  10. Mendonc, P.R.S., Padfield, D.R., Ross, J.C., Miller, J.V., Dutta, S. and Gautham, S.M., Quantification of Emphysema Severity by Histogram Analysis of CT Scans, Medical Image Computing and Computer-Assisted Intervention MICCAI, Springer-Verlag, pp. 738–744, 2005