Nonlinear total variation based noise removal algorithms
Proceedings of the eleventh annual international conference of the Center for Nonlinear Studies on Experimental mathematics : computational issues in nonlinear science: computational issues in nonlinear science
TV Based Image Restoration with Local Constraints
Journal of Scientific Computing
Speckle suppression in ultrasonic images based on undecimated wavelets
EURASIP Journal on Applied Signal Processing
A TV Based Restoration Model with Local Constraints
Journal of Scientific Computing
Multiplicative Noise Cleaning via a Variational Method Involving Curvelet Coefficients
SSVM '09 Proceedings of the Second International Conference on Scale Space and Variational Methods in Computer Vision
A New Total Variation Method for Multiplicative Noise Removal
SIAM Journal on Imaging Sciences
A Nonlinear Inverse Scale Space Method for a Convex Multiplicative Noise Model
SIAM Journal on Imaging Sciences
Variational denoising of partly textured images by spatially varying constraints
IEEE Transactions on Image Processing
Homotopy method for a mean curvature-based denoising model
Applied Numerical Mathematics
Adaptive Fractional-order Multi-scale Method for Image Denoising
Journal of Mathematical Imaging and Vision
A convex relaxation method for computing exact global solutions for multiplicative noise removal
Journal of Computational and Applied Mathematics
Image Restoration via Tight Frame Regularization and Local Constraints
Journal of Scientific Computing
An effective dual method for multiplicative noise removal
Journal of Visual Communication and Image Representation
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The Aubert-Aujol (AA) model is a variational method for multiplicative noise removal. In this paper, we study some basic properties of the regularization parameter in the AA model. We develop a method for automatically choosing the regularization parameter in the multiplicative noise removal process. In particular, we employ spatially varying regularization parameters in the AA model in order to restore more texture details of the denoised image. Experimental results are presented to demonstrate that the spatially varying regularization parameters method can obtain better denoised images than the other tested multiplicative noise removal methods.