## Abstract and Applied Analysis

### New Regularization Models for Image Denoising with a Spatially Dependent Regularization Parameter

#### Abstract

We consider simultaneously estimating the restored image and the spatially dependent regularization parameter which mutually benefit from each other. Based on this idea, we refresh two well-known image denoising models: the LLT model proposed by Lysaker et al. (2003) and the hybrid model proposed by Li et al. (2007). The resulting models have the advantage of better preserving image regions containing textures and fine details while still sufficiently smoothing homogeneous features. To efficiently solve the proposed models, we consider an alternating minimization scheme to resolve the original nonconvex problem into two strictly convex ones. Preliminary convergence properties are also presented. Numerical experiments are reported to demonstrate the effectiveness of the proposed models and the efficiency of our numerical scheme.

#### Article information

Source
Abstr. Appl. Anal., Volume 2013, Special Issue (2013), Article ID 729151, 15 pages.

Dates
First available in Project Euclid: 26 February 2014

https://projecteuclid.org/euclid.aaa/1393447479

Digital Object Identifier
doi:10.1155/2013/729151

Mathematical Reviews number (MathSciNet)
MR3134166

Zentralblatt MATH identifier
07095291

#### Citation

Ma, Tian-Hui; Huang, Ting-Zhu; Zhao, Xi-Le. New Regularization Models for Image Denoising with a Spatially Dependent Regularization Parameter. Abstr. Appl. Anal. 2013, Special Issue (2013), Article ID 729151, 15 pages. doi:10.1155/2013/729151. https://projecteuclid.org/euclid.aaa/1393447479

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