An efficient algorithm for adaptive total variation based image decomposition and restoration

Xinwu Liu ; Lihong Huang

International Journal of Applied Mathematics and Computer Science, Tome 24 (2014), p. 405-415 / Harvested from The Polish Digital Mathematics Library

Résumé

With the aim to better preserve sharp edges and important structure features in the recovered image, this article researches an improved adaptive total variation regularization and H −1 norm fidelity based strategy for image decomposition and restoration. Computationally, for minimizing the proposed energy functional, we investigate an efficient numerical algorithm-the split Bregman method, and briefly prove its convergence. In addition, comparisons are also made with the classical OSV (Osher-Sole-Vese) model (Osher et al., 2003) and the TV-Gabor model (Aujol et al., 2006), in terms of the edge-preserving capability and the recovered results. Numerical experiments markedly demonstrate that our novel scheme yields significantly better outcomes in image decomposition and denoising than the existing models.

Détail

Publié le : 2014-01-01

Zbl 1293.94017

EUDML-ID : urn:eudml:doc:271926

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