Greyscale Image Interpolation Using Mathematical Morphology

  • Alessandro Ledda
  • Hiêp Q. Luong
  • Wilfried Philips
  • Valérie De Witte
  • Etienne E. Kerre
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4179)


When magnifying a bitmapped image, we want to increase the number of pixels it covers, allowing for finer details in the image, which are not visible in the original image. Simple interpolation techniques are not suitable because they introduce jagged edges, also called “jaggies”.

Earlier we proposed the “mmint” magnification method (for integer scaling factors), which avoids jaggies. It is based on mathematical morphology. The algorithm detects jaggies in magnified binary images (using pixel replication) and removes them, making the edges smoother. This is done by replacing the value of specific pixels.

In this paper, we extend the binary mmint to greyscale images. The pixels are locally binarized so that the same morphological techniques can be applied as for mmint. We take care of the more difficult replacement of pixel values, because several grey values can be part of a jaggy. We then discuss the visual results of the new greyscale method.


Mathematical Morphology Foreground Pixel Corner Detection Current Pixel Image Interpolation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Alessandro Ledda
    • 1
  • Hiêp Q. Luong
    • 1
  • Wilfried Philips
    • 1
  • Valérie De Witte
    • 2
  • Etienne E. Kerre
    • 2
  1. 1.Ghent University, TELIN-IPIGentBelgium
  2. 2.Department of Applied Mathematics & Computer ScienceGhent UniversityGentBelgium

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