An Adaptive Total Generalized Variation Model with Augmented Lagrangian Method for Image Denoising

Chuan He,Changhua Hu,Xiaogang Yang,Huafeng He,Qi Zhang

doi:10.1155/2014/157893

Chuan He, Changhua Hu + Show 3 more

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https://doi.org/10.1155/2014/157893

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Abstract

We propose an adaptive total generalized variation (TGV) based model, aiming at achieving a balance between edge preservation and region smoothness for image denoising. The variable splitting (VS) and the classical augmented Lagrangian method (ALM) are used to solve the proposed model. With the proposed adaptive model and ALM, the regularization parameter, which balances the data fidelity and the regularizer, is refreshed with a closed form in each iterate, and the image denoising can be accomplished without manual interference. Numerical results indicate that our method is effective in staircasing effect suppression and holds superiority over some other state-of-the-art methods both in quantitative and in qualitative assessment.

Highlights

In the past few decades, many variation or partial differential equation (PDE) based restoration models [1,2,3,4,5,6,7] have been proposed to recover images from degraded observations, due to the ability of preserving significant image features such as edges or textures
We propose an adaptive total generalized variation (TGV) based model, aiming at achieving a balance between edge preservation and region smoothness for image denoising
On the other hand, compared with the existing total variation (TV)-based adaptive methods [10, 23, 24], we propose a more complicated adaptive method based on TGV, and it is apt to achieve more attractive results than the TV-based methods

Summary

Introduction

In the past few decades, many variation or partial differential equation (PDE) based restoration models [1,2,3,4,5,6,7] have been proposed to recover images from degraded observations, due to the ability of preserving significant image features such as edges or textures.

Methods

Results

Conclusion