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Applied Optics

Applied Optics


  • Vol. 39, Iss. 35 — Dec. 10, 2000
  • pp: 6633–6640

Noise and speckle reduction in synthetic aperture radar imagery by nonparametric Wiener filtering

Robert S. Caprari, Alvin S. Goh, and Emily K. Moffatt  »View Author Affiliations

Applied Optics, Vol. 39, Issue 35, pp. 6633-6640 (2000)

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We present a Wiener filter that is especially suitable for speckle and noise reduction in multilook synthetic aperture radar (SAR) imagery. The proposed filter is nonparametric, not being based on parametrized analytical models of signal statistics. Instead, the Wiener–Hopf equation is expressed entirely in terms of observed signal statistics, with no reference to the possibly unobservable pure signal and noise. This Wiener filter is simple in concept and implementation, exactly minimum mean-square error, and directly applicable to signal-dependent and multiplicative noise. We demonstrate the filtering of a genuine two-look SAR image and show how a nonnegatively constrained version of the filter substantially reduces ringing.

© 2000 Optical Society of America

OCIS Codes
(100.2000) Image processing : Digital image processing
(100.2980) Image processing : Image enhancement
(100.3020) Image processing : Image reconstruction-restoration
(280.6730) Remote sensing and sensors : Synthetic aperture radar

Original Manuscript: February 17, 2000
Revised Manuscript: June 27, 2000
Published: December 10, 2000

Robert S. Caprari, Alvin S. Goh, and Emily K. Moffatt, "Noise and speckle reduction in synthetic aperture radar imagery by nonparametric Wiener filtering," Appl. Opt. 39, 6633-6640 (2000)

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  1. R. Caprari, “Non-parametric Wiener filter for reducing noise on reproducible pure signals,” J. Phys. A 32, 3075–3094 (1999). [CrossRef]
  2. C. Oliver, S. Quegan, Understanding Synthetic Aperture Radar Images (Artech House, Boston, 1998).
  3. K. Rank, M. Lendl, R. Unbehauen, “Estimation of image noise variance,” IEE Proc. Vision Image Signal Process. 146, 80–84 (1999). [CrossRef]
  4. I. Birrer, E. Bracalente, G. Dome, J. Sweet, G. Berthold, “σ0 signature of the Amazon rain forest obtained from the Seasat scatterometer,” IEEE Trans. Geosci. Remote Sens. 20, 11–17 (1982). [CrossRef]
  5. D. Early, D. Long, “Azimuthal modulation of C-band scatterometer σ0 over southern ocean sea ice,” IEEE Trans. Geosci. Remote Sens. 35, 1201–1209 (1997). [CrossRef]
  6. J. Goodman, “Some fundamental properties of speckle,” J. Opt. Soc. Am. 66, 1145–1150 (1976). [CrossRef]
  7. J. Goodman, “A random walk through the field of speckle,” Opt. Eng. 86, 610–612 (1986).
  8. L. Porcello, N. Massey, R. Innes, J. Marks, “Speckle reduction in synthetic aperture radars,” J. Opt. Soc. Am. 66, 1305–1311 (1976). [CrossRef]
  9. F.-L. Li, C. Croft, D. Held, “Comparison of several techniques to obtain multiple-look SAR imagery,” IEEE Trans. Geosci. Remote Sens. 21, 370–375 (1983). [CrossRef]
  10. C. Helstrom, “Image restoration by the method of least squares,” J. Opt. Soc. Am. 57, 297–303 (1967). [CrossRef]
  11. S. Reichenbach, S. Park, “Small convolution kernels for high-fidelity image restoration,” IEEE Trans. Signal Process. 39, 2263–2274 (1991). [CrossRef]
  12. V. Frost, J. Stiles, K. Shanmugan, J. Holtzman, “A model for radar images and its application to adaptive digital filtering of multiplicative noise,” IEEE Trans. Pattern Anal. Mach. Intell. 4, 157–166 (1982). [CrossRef] [PubMed]
  13. E. Harvey, G. April, “Speckle reduction in synthetic-aperture-radar imagery,” Opt. Lett. 15, 740–742 (1990). [CrossRef] [PubMed]
  14. G. Franceschetti, V. Pascazio, G. Schirinzi, “Iterative homomorphic technique for speckle reduction in synthetic-aperture radar imaging,” J. Opt. Soc. Am. A 12, 686–694 (1995). [CrossRef]
  15. N. Stacy, M. Burgess, M. Muller, R. Smith, “Ingara: an integrated airborne imaging radar system,” in Proceedings of the International Geoscience and Remote Sensing Symposium (Institute of Electrical and Electronics Engineers, New York, 1996), Vol. 3, pp. 1618–1620.
  16. S.-S. Jiang, A. Sawchuk, “Noise updating repeated Wiener filter and other adaptive noise smoothing filters using local image statistics,” Appl. Opt. 25, 2326–2337 (1986). [CrossRef] [PubMed]
  17. R. Lagendijk, J. Biemond, D. Boekee, “Regularized iterative image restoration with ringing reduction,” IEEE Trans. Acoust. Speech Signal Process. 36, 1874–1888 (1988). [CrossRef]
  18. A. Tekalp, H. Kaufman, J. Woods, “Edge-adaptive Kalman filtering for image restoration with ringing suppression,” IEEE Trans. Acoust. Speech Signal Process. 37, 892–899 (1989). [CrossRef]
  19. Y. Cao, P. Eggermont, S. Terebey, “Cross Burg entropy maximization and its application to ringing suppression in image reconstruction,” IEEE Trans. Image Process. 8, 286–292 (1999). [CrossRef]
  20. R. Caprari, “Generalized matched filters and univariate Neyman–Pearson detectors for image target detection,” IEEE Trans. Inform. Theory 46, 1932–1937 (2000). [CrossRef]

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