An efficient two-phase L1-TV method for restoring blurred images with impulse noise
IEEE Transactions on Image Processing
SIAM Journal on Scientific Computing
SIAM Journal on Imaging Sciences
A Multi-Scale Vectorial Lτ-TV Framework for Color Image Restoration
International Journal of Computer Vision
SIAM Journal on Imaging Sciences
Augmented Lagrangian Method for Generalized TV-Stokes Model
Journal of Scientific Computing
A Total Variation-Based JPEG Decompression Model
SIAM Journal on Imaging Sciences
Proximity algorithms for the L1/TV image denoising model
Advances in Computational Mathematics
A fixed-point augmented Lagrangian method for total variation minimization problems
Journal of Visual Communication and Image Representation
Hybrid regularization image deblurring in the presence of impulsive noise
Journal of Visual Communication and Image Representation
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Image restoration based on an $\ell^1$-data-fitting term and edge preserving total variation regularization is considered. The associated nonsmooth energy minimization problem is handled by utilizing Fenchel duality and dual regularization techniques. The latter guarantee uniqueness of the dual solution and an efficient way for reconstructing a primal solution, i.e., the restored image, from a dual solution. For solving the resulting primal-dual system, a semismooth Newton solver is proposed and its convergence is studied. The paper ends with a report on restoration results obtained by the new algorithm for salt-and-pepper or random-valued impulse noise including blurring. A comparison with other methods is provided as well.