On Nonmonotone Chambolle Gradient Projection Algorithms for Total Variation Image Restoration
Journal of Mathematical Imaging and Vision
Removing Multiplicative Noise by Douglas-Rachford Splitting Methods
Journal of Mathematical Imaging and Vision
Multiplicative Noise Removal Using L1 Fidelity on Frame Coefficients
Journal of Mathematical Imaging and Vision
Total variation restoration of speckled images using a split-Bregman algorithm
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Multiplicative noise removal using variable splitting and constrained optimization
IEEE Transactions on Image Processing
A Variational Model to Remove the Multiplicative Noise in Ultrasound Images
Journal of Mathematical Imaging and Vision
A weberized total variation regularization-based image multiplicative noise removal algorithm
EURASIP Journal on Advances in Signal Processing
Multiplicative Noise Removal with Spatially Varying Regularization Parameters
SIAM Journal on Imaging Sciences
Primal and Dual Bregman Methods with Application to Optical Nanoscopy
International Journal of Computer Vision
Multiplicative noise removal via a novel variational model
Journal on Image and Video Processing - Special issue on emerging methods for color image and video quality enhancement
Journal of Mathematical Imaging and Vision
Fast algorithm for multiplicative noise removal
Journal of Visual Communication and Image Representation
A convex relaxation method for computing exact global solutions for multiplicative noise removal
Journal of Computational and Applied Mathematics
Nonconvex sparse regularizer based speckle noise removal
Pattern Recognition
Fast reduction of speckle noise in real ultrasound images
Signal Processing
Bregman operator splitting with variable stepsize for total variation image reconstruction
Computational Optimization and Applications
Journal of Computational and Applied Mathematics
An effective dual method for multiplicative noise removal
Journal of Visual Communication and Image Representation
Despeckling low SNR, low contrast ultrasound images via anisotropic level set diffusion
Multidimensional Systems and Signal Processing
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Multiplicative noise removal problems have attracted much attention in recent years. Unlike additive noise removal problems, the noise is multiplied to the orginal image, so almost all information of the original image may disappear in the observed image. The main aim of this paper is to propose and study a strictly convex objective function for multiplicative noise removal problems. We also incorporate the modified total variation regularization in the objective function to recover image edges. We develop an alternating minimization algorithm to find the minimizer of such an objective function efficiently and also show the convergence of the minimizing method. Our experimental results show that the quality of images denoised by the proposed method is quite good.