Multiplicative noise removal via a novel variational model

Authors:
Li-Li Huang;Liang Xiao;Zhi-Hui Wei
Affiliations:
School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing, China and Department of Information and Computing Science, Guangxi University of Technology, Liuzh ...;School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing, China;Department of Applied Mathematics, Nanjing University of Science and Technology, Nanjing, China
Venue:
Journal on Image and Video Processing - Special issue on emerging methods for color image and video quality enhancement
Year:
2010

Citing 20
Cited 1

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
Iterative methods for total variation denoising

SIAM Journal on Scientific Computing - Special issue on iterative methods in numerical linear algebra; selected papers from the Colorado conference
Variational Restoration and Edge Detection for Color Images

Journal of Mathematical Imaging and Vision
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)
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
PCA-based spatially adaptive denoising of CFA images for single-sensor digital cameras

IEEE Transactions on Image Processing
A New Total Variation Method for Multiplicative Noise Removal

SIAM Journal on Imaging Sciences
A Fast Algorithm for Edge-Preserving Variational Multichannel Image Restoration

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

SIAM Journal on Imaging Sciences
Two-stage image denoising by principal component analysis with local pixel grouping

Pattern Recognition
Removing Multiplicative Noise by Douglas-Rachford Splitting Methods

Journal of Mathematical Imaging and Vision
Iterative weighted maximum likelihood denoising with probabilistic patch-based weights

IEEE Transactions on Image Processing
Total variation restoration of speckled images using a split-Bregman algorithm

ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
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
The digital TV filter and nonlinear denoising

IEEE Transactions on Image Processing
Speckle reducing anisotropic diffusion

IEEE Transactions on Image Processing
On the estimation of the coefficient of variation for anisotropic diffusion speckle filtering

IEEE Transactions on Image Processing
Oriented Speckle Reducing Anisotropic Diffusion

IEEE Transactions on Image Processing

Total variation regularization algorithms for images corrupted with different noise models: a review

Journal of Electrical and Computer Engineering

Quantified Score

Hi-index	0.00

Visualization

Abstract

Multiplicative noise appears in various image processing applications, such as synthetic aperture radar, ultrasound imaging, single particle emission-computed tomography, and positron emission tomography. Hence multiplicative noise removal is of momentous significance in coherent imaging systems and various image processing applications. This paper proposes a nonconvex Bayesian type variational model for multiplicative noise removal which includes the total variation (TV) and the Weberized TV as regularizer. We study the issues of existence and uniqueness of a minimizer for this variational model. Moreover, we develop a linearized gradient method to solve the associated Euler-Lagrange equation via a fixed-point iteration. Our experimental results show that the proposed model has good performance.