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Computer Studies Image_Interpolation_using_Mathematical_M.pdf

A pixel replication m 4 b mm int g interpolated fig

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(a) Pixel replication ( M = 4) (b) mm int g interpolated Fig. 12. The interpolation with mm int g of a real life scene (a cut-out of the Lena image) 6 Conclusions We presented a greyscale solution for the binary morphological interpolation technique mm int . A temporary local binarization of the grey values, combined with a local majority ordering, makes it possible to perform the morphological hit-miss transform on the image. The grey values of the pixels that need to change value, are defined in terms of neighbouring pixel values. The visual quality of the new mm int g is very good for cartoon sprites and line graphics. Its results are in most cases visually better than those of hq . References 1. Lehmann, T., G¨ onner, C., Spitzer, K.: Survey: Interpolations Methods In Medical Image Processing. IEEE Transactions on Medical Imaging 18 (1999) 1049–1075 2. Allebach, J., Wong, P.: Edge-directed interpolation. In: Proceedings of the IEEE International Conference on Image Processing ICIP ’96. Volume 3., Lausanne, Switzerland (1996) 707–710 3. Li, X., Orchard, M.: New Edge-Directed Interpolation. IEEE Transactions on Image Processing 10 (2001) 1521–1527 4. Muresan, D., Parks, T.: Adaptively quadratic (AQua) image interpolation. IEEE Transactions on Image Processing 13 (2004) 690–698 5. Tschumperl´ e, D.: PDE’s Based Regularization of Multivalued Images and Appli- cations. PhD thesis, Universit´ e de Nice — Sophia Antipolis, Nice, France (2002) 6. Morse, B., Schwartzwald, D.: Isophote-Based Interpolation. In: Proceedings of the IEEE International Conference on Image Processing ICIP ’98, Chicago, Illinois, USA (1998) 227–231
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7. Luong, H., De Smet, P., Philips, W.: Image Interpolation using Constrained Adap- tive Contrast Enhancement Techniques. In: Proceedings of the IEEE International Conference on Image Processing ICIP ’05, Genova, Italy (2005) 998–1001 8. Albiol, A., Serra, J.: Morphological Image Enlargements. Journal of Visual Com- munication and Image Representation 8 (1997) 367–383 9. Honda, H., Haseyama, M., Kitajima, H.: Fractal Interpolation for Natural Images. In: Proceedings of the IEEE International Conference on Image Processing ICIP ’99. Volume 3., Kobe, Japan (1999) 657–661 10. Stepin, M.: hq3x Magnification Filter. (2003) 11. Freeman, W., Jones, T., Pasztor, E.: Example-Based Super-Resolution. IEEE Computer Graphics and Applications 22 (2002) 56–65 12. Ledda, A., Luong, H., Philips, W., De Witte, V., Kerre, E.: Image Interpolation using Mathematical Morphology. In: Proceedings of 2nd IEEE International Con- ference on Document Image Analysis for Libraries (DIAL’06), Lyon, France (2006) 358–367 13. Serra, J.: Image Analysis and Mathematical Morphology. Volume 1. Academic Press, New York (1982) 14. Soille, P.: Morphological Image Analysis: Principles and Applications. 2nd edn. Springer-Verlag (2003) 15. Haralick, R., Shapiro, L.: 5. In: Computer and Robot Vision. Volume 1. Addison- Wesley (1992) 16. Ronse, C.: A Lattice-Theoretical Morphological View on Template Extraction in Images. Journal of Visual Communication and Image Representation 7 (1996) 273–295 17. Ledda, A., Philips, W.: Majority Ordering for Colour Mathematical Morphology.
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  • Fall '18
  • William
  • Image processing, Mathematical morphology, Digital geometry

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