Residual whiteness principle for parameter-free image restoration

DOI10.1553/etna_vol53s329zbMath1455.94027OpenAlexW3020692621MaRDI QIDQ1988497

Alessandro Lanza, Monica Pragliola, Fiorella Sgallari

Publication date: 23 April 2020

Published in: ETNA. Electronic Transactions on Numerical Analysis (Search for Journal in Brave)

Full work available at URL: http://etna.mcs.kent.edu/volumes/2011-2020/vol53/abstract.php?vol=53&pages=329-351

zbMATH Keywords

variational methodsimage restorationregularization parameteralternating direction method of multipliersadditive white Gaussian noise

Mathematics Subject Classification ID

Numerical optimization and variational techniques (65K10) Image processing (compression, reconstruction, etc.) in information and communication theory (94A08)

Related Items (13)

Residual whiteness principle for automatic parameter selection in \(\ell_2-\ell_2\) image super-resolution problems ⋮ Automatic fidelity and regularization terms selection in variational image restoration ⋮ Whiteness-based parameter selection for Poisson data in variational image processing ⋮ A variable projection method for large-scale inverse problems with \(\ell^1\) regularization ⋮ Ternary image decomposition with automatic parameter selection via auto- and cross-correlation ⋮ Generalized Sparse Bayesian Learning and Application to Image Reconstruction ⋮ Parameter-free restoration of piecewise smooth images ⋮ ADMM-based residual whiteness principle for automatic parameter selection in single image super-resolution problems ⋮ Inducing sparsity via the horseshoe prior in imaging problems ⋮ On and Beyond Total Variation Regularization in Imaging: The Role of Space Variance ⋮ On the determination of Lagrange multipliers for a weighted Lasso problem using geometric and convex analysis techniques ⋮ A comparison of parameter choice rules for \(\ell^p\)-\(\ell^q\) minimization ⋮ Masked unbiased principles for parameter selection in variational image restoration under Poisson noise

Uses Software

• corner.m
• cosereg

Cites Work

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