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

Tian-Hui Ma,Xi-Le Zhao,Ting-Zhu Huang

doi:10.1155/2013/729151

Tian-Hui Ma, Xi-Le Zhao + Show 1 more

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https://doi.org/10.1155/2013/729151

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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.

Highlights

Image denoising is a fundamental problem in image processing and computer vision
The degraded images are corrupted by white Gaussian noise with the noise level 0.1, which are shown in Figures 3(a), 4(a), 5(a), and 6(a)
All of the parameters in these three models are optimized to achieve the best restoration with respect to the relative error (ReErr) and the signal-to-noise ratio (SNR) values

Summary

Introduction

Image denoising is a fundamental problem in image processing and computer vision. In many real-world applications, it forms a significant preliminary step for subsequent image processing operations, such as object recognition, medical image analysis, surveillance, and many more. Images are often corrupted by Gaussian noise. In this problem, the degradation process is modeled as f = uexact + η, (1). Where f, uexact, and η represent the observed image, the original image, and the additive white Gaussian noise, respectively. To obtain a reasonable approximated solution from (1), the regularization method, generally used as a numerical technique for stabilizing inverse problems, has been increasingly applied to image denoising over the past decades.

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Conclusion