We propose a variational method for recovering discrete sur- faces from noisy observations which promotes sparsity in the normal vari- ation more accurately than `1 norm (total variation) and `0 pseudo-norm regularization methods by incorporating a parameterized non-convex penalty function. This results in denoised surfaces with enhanced at regions and maximally preserved sharp features, including edges and corners. Unlike the classical two-steps mesh denoising approaches, we propose a unique, eective optimization model which is eciently solved by an instance of Alternating Direction Method of Multipliers. Experi- ments are presented which strongly indicate that using the sparsity-aided formulation holds the potential for accurate restorations even in the pres- ence of high noise.

Martin Huska, Serena Morigi, Giuseppe Antonio Recupero (2021). Sparsity-aided Variational Mesh Restoration. Berlin : Elmoataz Abderrahim, Fadili Jalal, Queau Yvain, Rabin Julien, Simon Loic [10.1007/978-3-030-75549-2_35].

Sparsity-aided Variational Mesh Restoration

Martin Huska;Serena Morigi;Giuseppe Antonio Recupero
2021

Abstract

We propose a variational method for recovering discrete sur- faces from noisy observations which promotes sparsity in the normal vari- ation more accurately than `1 norm (total variation) and `0 pseudo-norm regularization methods by incorporating a parameterized non-convex penalty function. This results in denoised surfaces with enhanced at regions and maximally preserved sharp features, including edges and corners. Unlike the classical two-steps mesh denoising approaches, we propose a unique, eective optimization model which is eciently solved by an instance of Alternating Direction Method of Multipliers. Experi- ments are presented which strongly indicate that using the sparsity-aided formulation holds the potential for accurate restorations even in the pres- ence of high noise.
2021
LNCS 12679: Scale Space and Variational Methods in Computer Vision
437
449
Martin Huska, Serena Morigi, Giuseppe Antonio Recupero (2021). Sparsity-aided Variational Mesh Restoration. Berlin : Elmoataz Abderrahim, Fadili Jalal, Queau Yvain, Rabin Julien, Simon Loic [10.1007/978-3-030-75549-2_35].
Martin Huska; Serena Morigi; Giuseppe Antonio Recupero
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/831913
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