Abstract
In this paper, we introduce a novel second-order regularizer, the Affine Total-Variation term, to capture the geometry of piecewise affine functions. The approach can be characterized by two convex decompositions of a given image into piecewise affine structure and texture and noise, respectively. A convergent multiplier-based method is presented for computing a global optimum by computationally cheap iterative steps. Experiments with images and vector fields validate our approach and illustrate the difference to classical TV denoising and decomposition.
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Yuan, J., Schnörr, C., Steidl, G. (2009). Total-Variation Based Piecewise Affine Regularization. In: Tai, XC., Mørken, K., Lysaker, M., Lie, KA. (eds) Scale Space and Variational Methods in Computer Vision. SSVM 2009. Lecture Notes in Computer Science, vol 5567. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02256-2_46
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DOI: https://doi.org/10.1007/978-3-642-02256-2_46
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-02255-5
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