Journal Article10.1016/J.CVIU.2006.11.015
Graph regularization for color image processing
TL;DR: A general discrete regularization framework defined on weighted graphs of arbitrary topologies which can be seen as a discrete analogue of classical regularization theory and uses a discrete definition of the p-Laplace operator.
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About: This article is published in Computer Vision and Image Understanding. The article was published on 01 Jul 2007. The article focuses on the topics: Regularization perspectives on support vector machines & Backus–Gilbert method.
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Citations
Nonlocal Discrete Regularization on Weighted Graphs: A Framework for Image and Manifold Processing
TL;DR: A nonlocal discrete regularization framework on weighted graphs of the arbitrary topologies for image and manifold processing, which leads to a family of simple and fast nonlinear processing methods based on the weighted -Laplace operator, parameterized by the degree of regularity, the graph structure and the graph weight function.
567
Time-Varying Graph Signal Reconstruction
TL;DR: A new batch reconstruction method of time-varying graph signals is proposed by exploiting the smoothness of the temporal difference signals, and the uniqueness of the solution to the corresponding optimization problem is theoretically analyzed.
165
Exemplar-Based Interpolation of Sparsely Sampled Images
Gabriele Facciolo,Pablo Arias,Vicent Caselles,Guillermo Sapiro +3 more
- 18 Aug 2009
TL;DR: Initial experimental results with the proposed framework, at very low sampling densities, are very encouraging, and some departures from the variational setting are explored, showing a remarkable ability to recover textures at low sampling density.
Stylizing face images via multiple exemplars
TL;DR: This work proposes an algorithm to stylize face images using multiple exemplars containing different subjects in the same style using a Markov Random Field, which enables accurate local energy transfer via Laplacian stacks.
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