SPATIAL-VARIANT MORPHOLOGICAL FILTERS WITH NONLOCAL-PATCH-DISTANCE-BASED AMOEBA KERNEL FOR IMAGE DENOISING

Authors

  • Shuo Yang Shanghai Jiao Tong University
  • Jian-Xun Li Shanghai Jiao Tong University

DOI:

https://doi.org/10.5566/ias.1098

Keywords:

amoeba morphology, geodesic distance, nonlocal morphology, patch distance, spatially-variant morphology

Abstract

Filters of the Spatial-Variant amoeba morphology can preserve edges better, but with too much noise being left. For better denoising, this paper presents a new method to generate structuring elements for Spatially-Variant amoeba morphology.  The amoeba kernel in the proposed strategy is divided into two parts: one is the patch distance based amoeba center, and another is the geodesic distance based amoeba boundary, by which the nonlocal patch distance and local geodesic distance are both taken into consideration. Compared to traditional amoeba kernel, the new one has more stable center and its shape can be less influenced by noise in pilot image. What’s more important is that the nonlocal processing approach can induce a couple of adjoint dilation and erosion, and combinations of them can construct adaptive opening, closing, alternating sequential filters, etc. By designing the new amoeba kernel, a family of morphological filters therefore is derived. Finally, this paper presents a series of results on both synthetic and real images along with comparisons with current state-of-the-art techniques, including novel applications to medical image processing and noisy SAR image restoration.

References

Angulo J (2011). Morphological bilateral filtering and spatially-variant adaptive structuring functions. ISMM 2011. LNCS, (6671):212–23.

Bouaynaya N, Charif-Chefchaouni M, Schonfeld D (2006). Spatially-variant morphological restoration and skeleton representation. IEEE T Image Process 15(11): 3579-91.

Buades A, Coll B, Morel J.M. (2005). A non-local algorithm for image denoising. in:CVPR2005, (2): 60 – 5.

Curic V, Luengo Hendriks, C.L., Borgefors, G (2012). Salience adaptive structuring elements. IEEE J Sel Top Signal Process 6(7): 809-19.

Debayle, J, Pinoli, J (2005). Spatially adaptive morphological image filtering using intrinsic structuring elements. Image Anal Stereol 24(3):145–58.

Grazzini J, Soillev P (2009). Edge-preserving smoothing using a similarity measure in adaptive geodesic neighbourhoods. Pattern Recogn 42(10): 2306–16.

Ikonen L, Toivanen P (2005). Shortest routes on varying height surfaces using gray-level distance transforms. Image Vision Comput 23(2): 133-41.

Lerallut R, Decenciere E, Meyer F (2007). Image filtering using morphological amoebas. Image Vision Comput 25(4):395–404.

Mittal A, Moorthy A.K., Bovik A.C. (2012). No-reference image quality assessment in the spatial domain. IEEE T Image Process 21(12): 4695-708.

Roerdink J (2009). Adaptive and group invariance in mathematical morphology. Proc. ICIP2009, pp.2253–6.

Salembier P (2009). Study on nonlocal morphological operators. Proc.ICIP2009, pp. 2269-72.

Sternberg S.R. (1986). Grayscale morphology. Comput Vision Graph (35): 333-55.

Velasco-Forero S, Angulo J (2013). On nonlocal mathematical morphology. ISMM2013 , (7883): 219-30.

Ta V.T., Elmoataz A, Lezoray O (2011). Nonlocal PDEs-based morphology on weighted graphs for image and data processing. IEEE T Image Process 20(6):1504-16.

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Published

2015-01-12

How to Cite

Yang, S., & Li, J.-X. (2015). SPATIAL-VARIANT MORPHOLOGICAL FILTERS WITH NONLOCAL-PATCH-DISTANCE-BASED AMOEBA KERNEL FOR IMAGE DENOISING. Image Analysis and Stereology, 34(1), 63–72. https://doi.org/10.5566/ias.1098

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Section

Original Research Paper