A convex nonlocal total variation regularization algorithm for multiplicative noise removal

Mingju Chen,Hua Zhang,Qiang Han,Chen Cheng Huang

doi:10.1186/s13640-019-0410-2

Abstract

This study proposes a nonlocal total variation restoration method to address multiplicative noise removal problems. The strictly convex, objective, nonlocal, total variation effectively utilizes prior information about the multiplicative noise and uses the maximum a posteriori estimator (MAP). An efficient iterative multivariable minimization algorithm is then designed to optimize our proposed model. Finally, we provide a rigorous convergence analysis of the alternating multivariable minimization iteration. The experimental results demonstrate that our proposed model outperforms other currently related models both in terms of evaluation indices and image visual quality.

Highlights

Image deblurring is an important task with numerous applications in both mathematics and image processing
Multiplicative noise often exists in many coherent imaging systems, such as ultrasonic imaging, optical coherence tomography (OCT), synthetic aperture radar (SAR), and so on [9,10,11]
5 Conclusion This study utilizes prior information and proposes a strictly convex nonlocal total variation (NLTV)-based multiplicative noise removal model based on the maximum prior estimate framework

Summary

Introduction

Image deblurring is an important task with numerous applications in both mathematics and image processing. Reference [18] integrated a quadratic penalty function into local TV model and proposed a new convex variational model for low multiplicative noise removal. Reference [26] applied the nonlocal total variation (NLTV) norm to the AA model and proposed a new NLTV-based method for multiplicative noise reduction. The AA model is efficient for multiplicative noise removal It has some problems since the local total variation regularization framework is exploited, such as smeared textures and the occurrence of staircase effects. This TV model is referred to as the exponential nonlocal-SO model [27]

The proposed model

Convergence analysis

Experiment results and discussions

Conclusion