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• W2901468968 abstract "Variational approaches are an established paradigm in the field of image processing. The non-convexity of the functional can be addressed by functional lifting and convex relaxation techniques, which aim to solve a convex approximation of the original energy on a larger space. However, so far these approaches have been limited to first-order , gradient-based regularizers such as the total variation . In this work, we propose a way to extend functional lifting to a second-order regularizer derived from the Laplacian. We prove that it can be represented efficiently and thus allows numerical optimization. We experimentally demonstrate the usefulness on a synthetic convex denoising problem and on synthetic as well as real-world image registration problems." @default.
• W2901468968 created "2018-11-29" @default.
• W2901468968 creator A5018608162 @default.
• W2901468968 creator A5027503123 @default.
• W2901468968 date "2018-01-01" @default.
• W2901468968 modified "2023-09-26" @default.
• W2901468968 title "Functional Lifting for Variational Problems with Higher-Order Regularization" @default.
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• W2901468968 doi "https://doi.org/10.1007/978-3-319-91274-5_5" @default.
• W2901468968 hasPublicationYear "2018" @default.
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