A New Total Variation Method for Multiplicative Noise Removal
By Yu-Mei Huang, Michael Kwok-Po Ng, You-wei Wen. SIAM Journal of Imaging Sciences, 2009
320 citations recorded by the source corpus.
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.
Subfields: Computational Mechanics, Computer Vision and Pattern Recognition
View Paper: https://www.semanticscholar.org/paper/b4d827bceb94b3d616f3ed060106a1072e1bce28
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computational Mechanics
- Luciano Alparone
- Tiziano Bianchi
- Fabrizio Argenti
- Alessandro Lapini
- Huanfeng Shen
- Hongyan Zhang
- Gabriele Steidl
- Qiangqiang Yuan
In Computer Vision and Pattern Recognition
- Huanfeng Shen
- Liangpei Zhang
- Qiangqiang Yuan
- Hongyan Zhang
- Tiziano Bianchi
- Alessandro Lapini
- Fabrizio Argenti
- Luciano Alparone
Citations in the corpus
- Image super-resolution: The techniques, applications, and future (2016, methodology): “As a result, other models take more complicated factors into consideration to better describe real cases, including different kinds of noise [52,76], dimensional complexity [51], domain transformation for the particular images [77], etc.”
- A Tutorial on Speckle Reduction in Synthetic Aperture Radar Images (2013, methodology): “When applying TV regularization in the logarithmic domain, convex TV problems can be obtained by applying different data fidelity terms, including the L2 norm [120], MAP based on Gamma distributed speckle [120], [124], a combination of the previous terms [121], the generalized Kullback-Leibler diver”
- Removing Multiplicative Noise by Douglas-Rachford Splitting Methods (2010, methodology): “The model of Shi and Osher was modified in [42] by adding a quadratic term to get a simpler alternating minimization algorithm.”