Noise Analysis for Depth Estimation

  • Aamir Saeed Malik
  • Tae-Sun Choi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4729)


Depth estimation is an important parameter for three-dimensional shape recovery. There are many factors affecting the depth estimation including luminance, texture reflectance, noise etc. In this paper, we limit our discussion to noise. We present noise analysis by first pre-filtering the noisy images using well known Wiener filter and then using a robust focus measure for depth estimation. That depth map can further be used in techniques and algorithms leading to recovery of three dimensional structure of the object. The focus measure is based on an optical transfer function implemented in the Fourier domain and its results are compared with the earlier focus measures and presented in this paper. The additive Gaussian noise is considered for noise analysis.


Noise Pre-Filtering Depth Map 


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  1. 1.
    Helmli, F.S., Scherer, S.: Adaptive Shape from Focus with an Error Estimation in Light Microscopy. In: 2nd Int’l Symposium on Image and Signal Processing and Analysis Croatia, pp. 188–193 (2001)Google Scholar
  2. 2.
    Nayar, S.K., Nakagawa, Y.: Shape from focus. IEEE Transactions on Pattern Analysis and Machine Intelligence 16(8), 824–831 (1994)CrossRefGoogle Scholar
  3. 3.
    Nayar, S.K., Noguchi, M., Watanabe, M., Nakagawa, Y.: Real time focus range sensors. IEEE Transactions on Pattern Analysis and Machine Intelligence 18(12), 1186–1198 (1996)CrossRefGoogle Scholar
  4. 4.
    Subbarao, M., Choi, T.-S.: Accurate recovery of three dimensional shape from image focus. IEEE Transactions on Pattern Analysis and Machine Intelligence 17(3), 266–274 (1995)CrossRefGoogle Scholar
  5. 5.
    Yun, J., Choi, T.-S.: Accurate 3-D Shape Recovery using Curved Window Focus Measure. In: IEEE International Conference on Image Processing, vol. 3, pp. 910–914. IEEE Computer Society Press, Los Alamitos (1999)Google Scholar
  6. 6.
    Choi, T.-S., Asif, M., Yun, J.: Three-dimensional shape recovery from focused image surface. In: IEEE International Conference of Acoustics, Speech and Signal Processing, vol. 6, pp. 3269–3272. IEEE Computer Society Press, Los Alamitos (1999)Google Scholar
  7. 7.
    Asif, M., Choi, T.-S.: Shape from focus using multilayer feedforward neural network. IEEE Transactions on Image Processing 10(11), 1670–1675 (2001)zbMATHCrossRefGoogle Scholar
  8. 8.
    Ahmad, M.B., Choi, T.-S.: A Heuristic approach for finding best focused shape. IEEE Transactions on Circuits and Systems for Video Technology 15(4), 566–574 (2005)CrossRefGoogle Scholar
  9. 9.
    Malik, A.S., Choi, T.-S.: A Novel Algorithm for Estimation of Depth Map using Image Focus for 3D Shape Recovery in the Presence of Noise. Pattern Recognition. Google Scholar
  10. 10.
    Malik, A.S., Choi, T.-S.: Consideration of Illumination Effects and Optimization of Window Size for Accurate Calculation of Depth Map for 3D Shape Recovery. Pattern Recognition 40(1), 154–170 (2007)zbMATHCrossRefGoogle Scholar
  11. 11.
    Poon, T.-C., Banerjee, P.P.: Contemporary optical image processing, 1st edn. Elsevier Science Ltd., New York (2001)Google Scholar
  12. 12.
    Grigorescu, C., Petkov, N., Westenberg, M.A.: Contour and boundary detection improved by surround suppression of texture edges. Image and Vision Computing 22(8), 609–622 (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Aamir Saeed Malik
    • 1
  • Tae-Sun Choi
    • 1
  1. 1.Gwangju Institute of Science and Technology, Oryong-Dong, Buk-Gu, Gwangju, 500712Korea

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