A weberized total variation regularization-based image multiplicative noise removal algorithm

Authors:
Liang Xiao;Li-Li Huang;Zhi-Hui Wei
Affiliations:
School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing, China;School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing, China;School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing, China
Venue:
EURASIP Journal on Advances in Signal Processing
Year:
2010

Citing 22
Cited 4

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
A Nonlinear Primal-Dual Method for Total Variation-Based Image Restoration

SIAM Journal on Scientific Computing
Image Sequence Analysis via Partial Differential Equations

Journal of Mathematical Imaging and Vision
On the Convergence of the Lagged Diffusivity Fixed Point Method in Total Variation Image Restoration

SIAM Journal on Numerical Analysis
An Algorithm for Total Variation Minimization and Applications

Journal of Mathematical Imaging and Vision
Image Processing And Analysis: Variational, Pde, Wavelet, And Stochastic Methods

Image Processing And Analysis: Variational, Pde, Wavelet, And Stochastic Methods
Mathematical Problems in Image Processing: Partial Differential Equations and the Calculus of Variations (Applied Mathematical Sciences)

Mathematical Problems in Image Processing: Partial Differential Equations and the Calculus of Variations (Applied Mathematical Sciences)
Edge-and-corner preserving regularization for image interpolation and reconstruction

Image and Vision Computing
Review: Adaptive total variation image deblurring: A majorization-minimization approach

Signal Processing
Speckle reduction by adaptive window anisotropic diffusion

Signal Processing
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 New Alternating Minimization Algorithm for Total Variation Image Reconstruction

SIAM Journal on Imaging Sciences
A Nonlinear Inverse Scale Space Method for a Convex Multiplicative Noise Model

SIAM Journal on Imaging Sciences
Removing Multiplicative Noise by Douglas-Rachford Splitting Methods

Journal of Mathematical Imaging and Vision
Visual compander in wavelet-based image coding

IEEE Transactions on Consumer Electronics
Perceptual digital watermark of images using wavelet transform

IEEE Transactions on Consumer Electronics
Speckle reducing anisotropic diffusion

IEEE Transactions on Image Processing
An EM algorithm for wavelet-based image restoration

IEEE Transactions on Image Processing
Bayesian wavelet-based image deconvolution: a GEM algorithm exploiting a class of heavy-tailed priors

IEEE Transactions on Image Processing
Oriented Speckle Reducing Anisotropic Diffusion

IEEE Transactions on Image Processing
Combined Curvelet Shrinkage and Nonlinear Anisotropic Diffusion

IEEE Transactions on Image Processing

Image variational denoising using gradient fidelity on curvelet shrinkage

EURASIP Journal on Advances in Signal Processing - Special issue on robust processing of nonstationary signals
Multiplicative noise removal via sparse and redundant representations over learned dictionaries and total variation

Signal Processing
Nonconvex sparse regularizer based speckle noise removal

Pattern Recognition
An effective dual method for multiplicative noise removal

Journal of Visual Communication and Image Representation

Quantified Score

Hi-index	0.00

Visualization

Abstract

Multiplicative noise removal is of momentous significance in coherent imaging systems and various image processing applications. This paper proposes a new nonconvex variational model for multiplicative noise removal under the Weberized total variation (TV) regularization framework. Then, we propose and investigate another surrogate strictly convex objective function for Weberized TV regularization-based multiplicative noise removal model. Finally, we propose and design a novel way of fast alternating optimizing algorithm which contains three subminimizing parts and each of them permits a closed-form solution. Our experimental results show that our algorithm is effective and efficient to filter out multiplicative noise while well preserving the feature details.