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Texture Reconstruction Guided by a High-Resolution Patch
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DOI：10.1109/TIP.2016.2627812.png)
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Abstract
En 中文
In this paper, we aim at super-resolving a low-resolution texture under the assumption that a high-resolution patch of the texture is available. To do so, we propose a variational method that combines two approaches that are texture synthesis and image reconstruction. The resulting objective function holds a nonconvex energy that involves a quadratic distance to the low-resolution image, a histogram-based distance to the high-resolution patch, and a nonlocal regularization that links the missing pixels with the patch pixels. As for the histogram-based measure, we use a sum of Wasserstein distances between the histograms of some linear transformations of the textures. The resulting optimization problem is efficiently solved with a primal-dual proximal method. Experiments show that our method leads to a significant improvement, both visually and numerically, with respect to the state-of-the-art algorithms for solving similar problems.
Keywords:
Super-resolution
texture synthesis
texture reconstruction
Wasserstein distance
histograms
nonlocal regularization
proximal algorithms
nonconvex optimization
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