Abstract

Pyramidal processing is a form of multiresolution image processing in which the image is decomposed into a sequence of images at different resolutions. Pyramidal processing aims to extract and interpret significant features of an image at different resolutions. Digital pyramidal image processing, because of the large number of convolution-type operations, is time consuming. On the other hand, optical pyramidal processors, described here, are preferable in real-time image-understanding applications because of their ease in performing convolution operations. Preliminary experimental results for optical Gaussian and Laplacian pyramidal image processing are presented.

© 1988 Optical Society of America

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References

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  1. P. J. Burt, in Multiresolution Image Processing and Analysis, A. Rosenfeld, ed. (Springer-Verlag, Berlin, 1984), Chap. 2.
  2. A. Rosenfeld, ed., Multiresolution Image Processing and Analysis (Springer-Verlag, Berlin, 1984), Chaps. 3–7.
    [CrossRef]
  3. J. W. Goodman, Introduction to Fourier Optics (McGraw-Hill, New York, 1968), Chap. 7.
  4. H.-K. Liu, T.-H. Chao, Proc. Soc. Photo-Opt. Instrum. Eng. 638, 55 (1986).
  5. J. J. Clark, P. D. Lawrence, in Multiresolution Image Processing and Analysis, A. Rosenfeld, ed. (Springer-Verlag, Berlin, 1984), pp. 148–168.
    [CrossRef]

1986

H.-K. Liu, T.-H. Chao, Proc. Soc. Photo-Opt. Instrum. Eng. 638, 55 (1986).

Burt, P. J.

P. J. Burt, in Multiresolution Image Processing and Analysis, A. Rosenfeld, ed. (Springer-Verlag, Berlin, 1984), Chap. 2.

Chao, T.-H.

H.-K. Liu, T.-H. Chao, Proc. Soc. Photo-Opt. Instrum. Eng. 638, 55 (1986).

Clark, J. J.

J. J. Clark, P. D. Lawrence, in Multiresolution Image Processing and Analysis, A. Rosenfeld, ed. (Springer-Verlag, Berlin, 1984), pp. 148–168.
[CrossRef]

Goodman, J. W.

J. W. Goodman, Introduction to Fourier Optics (McGraw-Hill, New York, 1968), Chap. 7.

Lawrence, P. D.

J. J. Clark, P. D. Lawrence, in Multiresolution Image Processing and Analysis, A. Rosenfeld, ed. (Springer-Verlag, Berlin, 1984), pp. 148–168.
[CrossRef]

Liu, H.-K.

H.-K. Liu, T.-H. Chao, Proc. Soc. Photo-Opt. Instrum. Eng. 638, 55 (1986).

Proc. Soc. Photo-Opt. Instrum. Eng.

H.-K. Liu, T.-H. Chao, Proc. Soc. Photo-Opt. Instrum. Eng. 638, 55 (1986).

Other

J. J. Clark, P. D. Lawrence, in Multiresolution Image Processing and Analysis, A. Rosenfeld, ed. (Springer-Verlag, Berlin, 1984), pp. 148–168.
[CrossRef]

P. J. Burt, in Multiresolution Image Processing and Analysis, A. Rosenfeld, ed. (Springer-Verlag, Berlin, 1984), Chap. 2.

A. Rosenfeld, ed., Multiresolution Image Processing and Analysis (Springer-Verlag, Berlin, 1984), Chaps. 3–7.
[CrossRef]

J. W. Goodman, Introduction to Fourier Optics (McGraw-Hill, New York, 1968), Chap. 7.

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Figures (5)

Fig. 1
Fig. 1

Coherent optical parallel pyramidal image-processing system. N identical Fourier-transform subsystems are used in parallel. For inputs, N identical image copies are used. In the first Fourier-lens back focal plane of each subsystem, a spatial low-pass or bandpass filter Fi with 1 < i < N is placed. The generated GPI’s (LPI’s) are collected by a digital image-postprocessor array.

Fig. 2
Fig. 2

First four binary GPI levels. (a) First-level image g1, (b) second-level image g2, (c) third-level image g3, (d) fourth-level image g4.

Fig. 3
Fig. 3

First three binary LPI levels of Fig. 2. (a) First-level image l1, (b) second-level image l2, (c) third-level image

Fig. 4
Fig. 4

First three levels of the zero-crossing LPI’s. (a) First-level, (b) second level, (c) third level.

Fig. 5
Fig. 5

Edge-enhanced copies of the binary GPI’s of Fig. 2. (a) First level, (b) second level, (c) third level, (d) fourth level.

Tables (1)

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Table 1 Gradient Masks Fx and Fy and Laplacian Mask E

Equations (1)

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f C i = D i / 2 λ f , D i / D i 1 = 2 ,

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