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Plenary Lecture

Image Denoising: From Multiresolution frameworks to Variational frameworks




Professor Sergio Amat
Head of the Department of Applied Mathematics and Statistics
U.P. Cartagena
SPAIN
Email: sergio.amat@upct.es


Abstract: In order to reduce the noise that perturb an image we can find two general approaches: Multiresolution frameworks and Variational frameworks.
In the linear case, both frameworks are related. But this is not the best situation in practice.
Our goal in this lecture is to review some nonlinear techniques in both framewors and their relations.
 

Brief Biography of the Speaker:

  1. Sergio Amat Plata, Elda (Alicante) (Spain), 23-3-73.

  2. MS and BS in Mathematics from Universitat de Val`encia in 1996. PhD in Mathematics from Universitat de Val`encia in 2001.

  3. PhD Visiting: Paris VI 1998-1999, Marseille 1998-1999.

  4. Post-doctoral and Visiting Faculty positions: EGIM, LATP IMT, Technopole de Chateau-Gombert Marseille (France) 2002-2003-2004-2005, U.Castilla La Mancha (Spain) 2003-2005-2006.

  5. Head of the Department of Applied Mathematics and Statistics (U.P. Cartagena) (Spain).

  6. More than 70 papers with referee process.

  7. More than 50 communications in congress.

  8. 10 PhD students.

  9. 1 Post-Doc student.

  10. He has participated in 2 regional, 3 national and 2 European projects.

  11. He is member of the executive board of SEMA (Spanish Society of Applied Mathematics).

  12. Head of the research group: Differential Equations and Numerical Analysis.

  13. Participation in the organization of several congress.

  14. Associated Editor of Journal of The Franklin Instituted.

  15. Associated Editor of Applied Mathematics and Computation.

  16. Associated Editor of Journal of Mathematical Sciences: Advances and Applications.

  17. Associated Editor of WSEAS.

  18. Referee and reviewer of several journals.

  19. Coordinator of several PhD programs.

  20. Coordinator of the department’s publications and conferences.

  21. His research interests include:
    - Nonlinear Reconstructions.
    - Multiresolution and wavelets algorithms.
    - Iterative schemes for nonlinear equations.
    - Numerical approximation of conservation laws.
     

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