These notes address various theoretical and practical topics related to Total Variation based image reconstruction. They focus first on some theoretical results on functions which minimize the total variation, and in a second part, describe a few standard and less standard algorithms to minimize the total variation in a finite-differences setting, with a series of applications from simple denoising to stereo, or deconvolution issues, and even more exotic uses like the minimization of minimal partition problems.

An introduction to Total Variation for Image Analysis

NOVAGA, MATTEO;
2010

Abstract

These notes address various theoretical and practical topics related to Total Variation based image reconstruction. They focus first on some theoretical results on functions which minimize the total variation, and in a second part, describe a few standard and less standard algorithms to minimize the total variation in a finite-differences setting, with a series of applications from simple denoising to stereo, or deconvolution issues, and even more exotic uses like the minimization of minimal partition problems.
2010
Theoretical Foundations and Numerical Methods for Sparse Recovery, in Radon Series on Computational and Applied Mathematics
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11577/2480002
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