Decoupling noise and features via weighted ℓ 1 -analysis compressed sensing

DOI10.1145/2557449zbMath1322.68238OpenAlexW2016533289MaRDI QIDQ4981900

Jiansong Deng, Zhou-Wang Yang, Falai Chen, Li-Gang Liu, Rui-Min Wang

Publication date: 23 March 2015

Published in: ACM Transactions on Graphics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1145/2557449

zbMATH Keywords

denoisingasymptotic optimalitysharp feature\(\ell_1\)-analysis compressed sensing

Mathematics Subject Classification ID

Computing methodologies for image processing (68U10) Computer graphics; computational geometry (digital and algorithmic aspects) (68U05) Image processing (compression, reconstruction, etc.) in information and communication theory (94A08)

Related Items (7)

Sparse RBF surface representations ⋮ Robust mesh denoising via vertex pre-filtering and \(L_1\)-median normal filtering ⋮ Denoising point sets via \(L_0\) minimization ⋮ Coupling time-varying modal analysis and FEM for real-time cutting simulation of objects with multi-material sub-domains ⋮ Sharp feature consolidation from raw 3D point clouds via displacement learning ⋮ A novel anisotropic second order regularization for mesh denoising ⋮ A Novel Mesh Denoising Method Based on Relaxed Second-Order Total Generalized Variation

Uses Software

• ARPACK

Cites Work

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• Compressed sensing with coherent and redundant dictionaries
• Feature-preserving mesh denoising based on vertices classification
• Enhancing sparsity by reweighted \(\ell _{1}\) minimization
• Decoding by Linear Programming
• ARPACK Users' Guide
• The Kolmogorov-Smirnov Test for Goodness of Fit
• Compressed sensing

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