Skip to main content
Log in

Reversible Interpolation of Vectorial Images by an Anisotropic Diffusion-Projection PDE

  • Published:
International Journal of Computer Vision Aims and scope Submit manuscript

Abstract

In this paper, a nonlinear model for the interpolation of vector-valued images is proposed. This model is based on an anisotropic diffusion PDE and performs an interpolation that is reversible. The interpolation solution is restricted to the subspace of functions that can recover the discrete input image, after an appropriate smoothing and sampling. The proposed nonlinear diffusion flow lies on this subspace while its strength and anisotropy adapt to the local variations and geometry of image structures. The derived method effectively reconstructs the real image structures and yields a satisfactory interpolation result. Compared to classic and other existing PDE-based interpolation methods, our proposed method seems to increase the accuracy of the result and to reduce the undesirable artifacts, such as blurring, ringing, block effects and edge distortion. We present extensive experimental results that demonstrate the potential of the method as applied to graylevel and color images.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  • Aly, H. A., & Dubois, E. (2005). Image up-sampling using total-variation regularization with a new observation model. IEEE Transactions on Image Processing, 14(10), 1647–1659.

    Article  MathSciNet  Google Scholar 

  • Belahmidi, A., & Guichard, F. (2004). A partial differential equation approach to image zoom. In Proceedings of the international conference on image processing (Vol. 1, pp. 649–652).

  • Bertalmio, M., Sapiro, G., Caselles, V., & Ballester, C. (2000). Image inpainting. In Proceedings of the SIGGRAPH 2000 (pp. 417–424).

  • Caselles, V., Morel, J. M., & Sbert, C. (1998). An axiomatic approach to image interpolation. IEEE Transactions on Image Processing, 7(3), 376–386.

    Article  MATH  MathSciNet  Google Scholar 

  • Chan, T. F., & Shen, J. (2002). Mathematical models for local nontexture inpaintings. SIAM Journal on Applied Mathematics, 62(3), 1019–1043.

    Article  MATH  MathSciNet  Google Scholar 

  • Dudgeon, D. E., & Mersereau, R. M. (1984). Multidimensional digital signal processing. New York: Prentice-Hall.

    MATH  Google Scholar 

  • Guichard, F., & Malgouyres, F. (1998). Total variation based interpolation. In Proceedings of the EUSIPCO (Vol. 3, pp. 1741–1744).

  • Malgouyres, F., & Guichard, F. (2001). Edge direction preserving image zooming: a mathematical and numerical analysis. SIAM Journal on Numerical Analysis, 39(1), 1–37.

    Article  MATH  MathSciNet  Google Scholar 

  • Meijering, E. (2002). A chronology of interpolation: from ancient astronomy to modern signal and image processing. Proceedings of the IEEE, 90(3), 319–342.

    Article  Google Scholar 

  • Naylor, A. W., & Sell, G. R. (1982). Linear operator theory in engineering and science. Berlin: Springer.

    MATH  Google Scholar 

  • Oppenheim, A. V., Willsky, A. S., & Young, I. T. (1984). Signals and systems. Berlin: Prentice Hall.

    Google Scholar 

  • Perona, P., & Malik, J. (1990). Scale space and edge detection using anisotropic diffusion. IEEE Transactions on Pattern Analysis and Machine Intelligence, 12(7), 629–639.

    Article  Google Scholar 

  • Roussos, A., & Maragos, P. (2007) Vector-valued image interpolation by an anisotropic diffusion-projection PDE. In Lecture notes in computer science : Vol. 4485. Scale space and variational methods in computer vision, first international conference, SSVM 2007 proceedings (pp. 104–115). Berlin: Springer.

    Chapter  Google Scholar 

  • Rudin, L., Osher, S., & Fatemi, E. (1992). Nonlinear total variation based noise removal algorithms. Physica D, 60, 259–268.

    Article  MATH  Google Scholar 

  • Tschumperlé, D. (2002). PDE’s based regularization of multivalued images and applications. PhD thesis, Univ. of Nice-Sophia Antipolis.

  • Tschumperlé, D., & Deriche, R. (2005). Vector-valued image regularization with PDE’s: a common framework for different applications. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(4), 506–517.

    Article  Google Scholar 

  • Wang, Z., Bovik, A., Sheikh, H., & Simoncelli, E. (2004). Image quality assessment: from error visibility to structural similarity. IEEE Transactions on Image Processing, 13(4), 600–612.

    Article  Google Scholar 

  • Weickert, J. (1998). Anisotropic diffusion in image processing. Stuttgart: Teubner.

    MATH  Google Scholar 

  • Weickert, J., & Welk, M. (2006) Tensor field interpolation with PDEs. In Visualization and processing of tensor fields (pp. 315–325). Berlin: Springer.

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Anastasios Roussos.

Additional information

The authors acknowledge the financial support of the Future and Emerging Technologies (FET) programme ‘ASPI’ within the Sixth Framework Programme for Research of the European Commission, under FET-Open contract No. 021324.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Roussos, A., Maragos, P. Reversible Interpolation of Vectorial Images by an Anisotropic Diffusion-Projection PDE. Int J Comput Vis 84, 130–145 (2009). https://doi.org/10.1007/s11263-008-0132-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11263-008-0132-x

Keywords

Navigation