Multiscale wedgelet denoising algorithms

L. Demaret; F. Friedrich; H. Fuehr; T. Szygowski

doi:10.1117/12.612888

Translator Disclaimer

17 September 2005 Multiscale wedgelet denoising algorithms

L. Demaret, F. Friedrich, H. Fuehr, T. Szygowski

Author Affiliations +

Proceedings Volume 5914, Wavelets XI; 59140X (2005) https://doi.org/10.1117/12.612888
Event: Optics and Photonics 2005, 2005, San Diego, California, United States

Abstract

We present a heuristic algorithm for the choice of the wedgelet regularization parameter for the purpose of denoising in the case where the noise variance σ² is not known. Numerical experiments comparing wavelet thresholding with wedgelet denoising, and with the related schemes quadtree approximation and platelet approximation, allow to assess the respective strengths of the different approaches. For small values of σ², wavelets are clearly superior to wedgelets, and they are better at restoring textured regions. For large σ², or for images of a predominantly geometric nature, wedgelets yield consistently better results. Moreover, the tests reveal that the heuristic algorithm is quite effective in choosing the regularization parameter.

Citation Download Citation

L. Demaret, F. Friedrich, H. Fuehr, and T. Szygowski "Multiscale wedgelet denoising algorithms", Proc. SPIE 5914, Wavelets XI, 59140X (17 September 2005); https://doi.org/10.1117/12.612888

ACCESS THE FULL ARTICLE