The use of the L-curve in the regularization of discrete ill-posed problems
SIAM Journal on Scientific Computing
Tikhonov regularization and the L-curve for large discrete ill-posed problems
Journal of Computational and Applied Mathematics - Special issue on numerical analysis 2000 Vol. III: linear algebra
An Algorithm for Total Variation Minimization and Applications
Journal of Mathematical Imaging and Vision
A TV Based Restoration Model with Local Constraints
Journal of Scientific Computing
A New Total Variation Method for Multiplicative Noise Removal
SIAM Journal on Imaging Sciences
Removing Multiplicative Noise by Douglas-Rachford Splitting Methods
Journal of Mathematical Imaging and Vision
Multiplicative noise removal using variable splitting and constrained optimization
IEEE Transactions on Image Processing
Restoration of images based on subspace optimization accelerating augmented Lagrangian approach
Journal of Computational and Applied Mathematics
A New TV-Stokes Model with Augmented Lagrangian Method for Image Denoising and Deconvolution
Journal of Scientific Computing
Bregman operator splitting with variable stepsize for total variation image reconstruction
Computational Optimization and Applications
Hi-index | 7.29 |
Owing to the edge preserving ability and low computational cost of the total variation (TV), variational models with the TV regularizer have been widely investigated in the field of multiplicative noise removal. The key points of the successful application of these models lie in: the optimal selection of the regularization parameter which balances the data-fidelity term with the TV regularizer, the efficient algorithm to compute the solution. In this paper, we propose two fast algorithms based on the linearized technique, which are able to estimate the regularization parameter and recover the image simultaneously. In the iteration step of the proposed algorithms, the regularization parameter is adjusted by a special discrepancy function defined for multiplicative noise. The convergence properties of the proposed algorithms are proved under certain conditions, and numerical experiments demonstrate that the proposed algorithms overall outperform some state-of-the-art methods in the PSNR values and computational time.