We propose and state a novel scheme for image magnification. It is formulated as a minimization problem which incorporates a data fidelity and a regularization term. Data fidelity is modeled using a wavelet transformation operator while the Total Generalized Variation functional of second order is applied for regularization. Well-posedness is obtained in a function space setting and an efficient numerical algorithm is developed. Numerical experiments confirm a high quality of the magnified images. In particular, with an appropriate choice of wavelets, geometrical information is preserved. © 2013 Springer-Verlag.
CITATION STYLE
Bredies, K., & Holler, M. (2013). A TGV regularized wavelet based zooming model. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7893 LNCS, pp. 149–160). https://doi.org/10.1007/978-3-642-38267-3_13
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