A Nonlinear Multigrid Method for Total Variation Minimization from Image Restoration
Journal of Scientific Computing
Acceleration methods for image restoration problem with different boundary conditions
Applied Numerical Mathematics
Convergence of Fixed Point Iteration for Modified Restoration Problems
Journal of Mathematical Imaging and Vision
SSVM '09 Proceedings of the Second International Conference on Scale Space and Variational Methods in Computer Vision
A lattice Boltzmann method for image denoising
IEEE Transactions on Image Processing
Active multispectral illumination and image fusion for retinal microsurgery
IPCAI'10 Proceedings of the First international conference on Information processing in computer-assisted interventions
A Multilevel Algorithm for Simultaneously Denoising and Deblurring Images
SIAM Journal on Scientific Computing
Journal of Mathematical Imaging and Vision
Geometry of total variation regularized Lp-model
Journal of Computational and Applied Mathematics
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In this paper, we apply a fixed point method to solve the total variation-based image denoising problem. An algebraic multigrid method is used to solve the corresponding linear equations. Krylov subspace acceleration is adopted to improve convergence in the fixed point iteration. A good initial guess for this outer iteration at finest grid is obtained by combining fixed point iteration and geometric multigrid interpolation successively from the coarsest grid to the finest grid. Numerical experiments demonstrate that this method is efficient and robust even for images with large noise-to-signal ratios.