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Morphological operators

Morphological operators

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The author and colleagues have already proved that mediated morphological filters (MEDMFs) remove speckle, Gaussian or salt&pepper noise better than classical morphological filters (MFs) and linear methods. They have also demonstrated the dominance of MEDMFs in a multiple noisy environment compared with MFs and linear filters. Here, they describe novel new research that has led to new morphological operators being successfully devised, the performance of which is better than MEDMFs for both single and multiple denoising. The new operators employ a special combination of weighted median filters and MFs.

References

    1. 1)
      • P. Maragos , R.W. Schafer . Morphological filters – Pt I: Their set-theoretic analysis and relations to linear shift-invariant filters. IEEE Trans. Acoust. Speech Signal Process. , 8 , 1153 - 1169
    2. 2)
      • M. Duff , M. Duff . (1979) Parallel processors for digital image processing, Advances in digital image processing.
    3. 3)
      • M.H. Sedaaghi . ECG wave detection using morphological filters. Appl. Signal Process. , 182 - 194
    4. 4)
      • Sedaaghi, M.H., Dai, R., Khosravi, M.: `Mediated morphological filters', Proc. of 2001 Int. Conf. on Image Processing, October 2001, Thessaloniki, Greece, IEEE Signal Processing Society, III, p. 692–695.
    5. 5)
      • J. Serra . (1992) Image analysis and mathematical morphology.
    6. 6)
      • P. Maragos . A representation theory for morphological image and signal processing. IEEE Trans. Pattern Anal. Mach. Intell. , 6 , 586 - 599
    7. 7)
      • J.C. Klein , L. Cahn , C. Ray , G.H. Urban . The texture analyser. J. Microsc. , 349 - 356
    8. 8)
      • F. Gerritsen , L.G. Aardema . Design and use of DIP-1: a last flexible and dynamically microprogrammable image processor. Pattern Recognit. , 319 - 330
    9. 9)
      • M.H. Sedaaghi , Q.H. Wu . Weighted morphological filter. Electron. Lett. , 16 , 1566 - 1567
    10. 10)
      • Heijmans, H.J.A.M.: `Morphological filters', Proc. of Summer School on Morphological Image and Signal Processing, 1995, Zakopane, Poland.
    11. 11)
      • S.K. Mitra , G.L. Sicuranza . (2001) Nonlinear image processing.
    12. 12)
      • R.M. Haralick , S.R. Sternberg , X. Zhuang . Imageanalysis using mathematical morphology. IEEE Trans. Pattern Anal.Mach. Intell. , 4 , 532 - 550
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