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Mesh Denoising using Extended ROF Model with L 1 Fidelity
Author(s) -
Wu Xiaoqun,
Zheng Jianmin,
Cai Yiyu,
Fu ChiWing
Publication year - 2015
Publication title -
computer graphics forum
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.578
H-Index - 120
eISSN - 1467-8659
pISSN - 0167-7055
DOI - 10.1111/cgf.12743
Subject(s) - regularization (linguistics) , polygon mesh , outlier , noise reduction , fidelity , computer science , algorithm , noise (video) , artificial intelligence , augmented lagrangian method , feature (linguistics) , pattern recognition (psychology) , mathematics , computer vision , mathematical optimization , image (mathematics) , telecommunications , linguistics , philosophy , computer graphics (images)
This paper presents a variational algorithm for feature‐preserved mesh denoising. At the heart of the algorithm is a novel variational model composed of three components: fidelity, regularization and fairness, which are specifically designed to have their intuitive roles. In particular, the fidelity is formulated as an L 1 data term, which makes the regularization process be less dependent on the exact value of outliers and noise. The regularization is formulated as the total absolute edge‐lengthed supplementary angle of the dihedral angle, making the model capable of reconstructing meshes with sharp features. In addition, an augmented Lagrange method is provided to efficiently solve the proposed variational model. Compared to the prior art, the new algorithm has crucial advantages in handling large scale noise, noise along random directions, and different kinds of noise, including random impulsive noise, even in the presence of sharp features. Both visual and quantitative evaluation demonstrates the superiority of the new algorithm.