On the Properties of Some Adaptive Morphological Filters for Salt and Pepper Noise Removal


  • Marisol Mares-Javier Facultad de Ciencias Físico Matemáticas Benemérita Universidad Autónoma de Puebla
  • Carlos Guillén-Galván Facultad de Ciencias Físico Matemáticas Benemérita Universidad Autónoma de Puebla
  • Rafael Lemuz-López Computer Science Faculty, BUAP University https://orcid.org/0000-0002-2139-9052
  • Johan Debayle Mines Saint-Etienne, CNRS, UMR 5307 LGF, Centre SPIN, 158 cours Fauriel, 42023 Saint-Etienne Cedex 2, France




adaptive morphological filters, grayscale images, noise removal


Mathematical Morphology (MM) is a tool that can be applied to many digital image processing tasks that include the reduction of impulsive or salt and pepper noise in grayscale images. The morphological filters used for this task are filters resulting from two basic operators: erosion and dilation. However, when the level of contamination of the image is higher, these filters tend to distort the image. In this work we propose a pair of operators with properties, that better adapt to impulsive noise than other classical morphological filters, it is demonstrated to be increasing idempotent morphological filters. Furthermore, the proposed pair turns out to be a ∧-filter and a ∨-filter which allow to build morphological openings and closings. Finally, they are compared with other filters of the state-of-the-art such as: SMF, DBAIN, AMF and NAFSM, and have shown a better performance in time-quality ratio when the noise level is above 50%.


Curic V, Landstrom A, Thurley MJ, Hendriks CL (2014). Adaptive mathematical morphology–a survey of the field. Pattern Recognition Letters 47:18–28. https://doi.org/10.1016/j.patrec.2014.02.022.

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


Debayle J, Pinoli JC (2005). Adaptive-Neighborhood Mathematical Morphology and its Applications to Image Filtering and Segmentation. In 9th European Congress on Stereology and Image Analysis (ECSIA) 2:123–30. https://ieeexplore.ieee.org/document/1530447.

Debayle J, Pinoli JC (2009). General Adaptive Neighborhood Mathematical Morphology. In IEEE International Conference on

Image Processing (ICIP): 2249–52. https://https://ieeexplore.ieee.org/document/5413979.

Angulo J, Velesco-Forero S. (2011). Structurally Adaptive Mathematical Morphology Based on Nonlinear Scale-Space Decompositions. Image Analysis & Stereology 30(2): 111–22.


Legaz-Aparicio AG, Verd´u-Monedero R, Angulo J (2018). Adaptive morphological filters based on a multiple orientation vector field dependent on image local features. Journal of Computational

and Applied Mathematics 330: 965–81.


Huang T, Yang G, Tang G (1979). “A fast two-dimensional median filtering algorithm”. IEEE Trans Acoust Speech, Signal Processing 27(1):13–18.


Ibrahim H, Kong NSP, Ng TF (2008). Simple adaptive median filter for the removal of impulse noise from highly corrupted images.

IEEE Trans Consum Electron 54(4):1920–27.


Maragos P, Schafer R (1987). Morphological filters–Part I: Their set-theoretic analysis and relations to linear shift-invariant

filters. IEEE Transactions on Acoustics, Speech, and Signal Process 35(8):1153–69.


Mukhopadhyay S, Chanda B (2002). An edge

preserving noise smoothing technique using multiscale morphology. Signal Process

(4):527–44. https://doi.org/10.1016/S0165-


Oh J, Chaparro LF (1998). Adaptive fuzzy morphological filtering of images. In Proceedings of the 1998 IEEE International Conference

on Acoustics, Speech and Signal Processing, ICASSP’98 (Cat. No. 98CH36181). 5: 2901–04.


Serra J (1983). Image analysis and mathematical morphology. Academic Press.

Serra J, Vicent L (1992). An overview of

morphological filtering. Circ Syst Signal Pr 11(1):47–108.


Song J, Delp EJ (1990). The analysis of morphological filters with multiple structuring elements. Computer Vision, Graphics, and Image Processing 50(3):308–28. https://doi.org/10.1016/0734-


Srinivasan KS, Ebenezer D (2007). A new fast and efficient decision–based algorithm for removal of high–density impulse noises.

IEEE Signal Proc Let 14(3):189–92.


Toh KKV, and Mat Isa NA (2010). Noise Adaptive Fuzzy Switching Median Filter for Salt-and-Pepper Noise Reduction. IEEE Signal Proc Let, 17(3): 281-84. https://ieeexplore.ieee.org/document/5356178.

Wang Z, Zhang D (1999). Progressive switching median filter for the removal of impulse noise from highly corrupted images. IEEE T Circuits-II 46(1):78–80.


Wang Z, Bovik AC, Sheikh HR, Simoncelli EP (2004). Image quality assessment: from error visibility to structural similarity.

IIEEE T Image Process 13(4): 600–12.


Zhao Y, Li D, Li Z (2007). Performance enhancement and analysis of an adaptive median filter. In 2007 Second International Conference on Communications and Networking in China 651–53.





How to Cite

Mares-Javier, M., Guillén-Galván, C., Lemuz-López, R., & Debayle, J. (2021). On the Properties of Some Adaptive Morphological Filters for Salt and Pepper Noise Removal. Image Analysis and Stereology, 40(1), 29–38. https://doi.org/10.5566/ias.2418



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