Adaptive directional total variation denoising model based on image local direction

TANG Ling; CHEN Ming-ju

doi:10.3788/YJYXS20163105.0477

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Adaptive directional total variation denoising model based on image local direction

Display and Image | 更新时间：2020-08-12

- Adaptive directional total variation denoising model based on image local direction
- Chinese Journal of Liquid Crystals and Displays Vol. 31, Issue 5, Pages: 477-483(2016)
- 作者机构：
  
  四川理工学院自动化与电子信息学院, 四川自贡 643000
- 作者简介：
- 基金信息：
- DOI：10.3788/YJYXS20163105.0477
  CLC： TN911.73
- Received：14 July 2015，
  
  Revised：23 July 2015，
  
  Published：05 May 2016
- 稿件说明：
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唐玲, 陈明举,. 基于图像局部方向特性的自适应全变分去噪模型[J]. 液晶与显示, 2016,31(5): 477-483

TANG Ling, CHEN Ming-ju,. Adaptive directional total variation denoising model based on image local direction[J]. , 2016,31(5): 477-483
唐玲, 陈明举,. 基于图像局部方向特性的自适应全变分去噪模型[J]. 液晶与显示, 2016,31(5): 477-483 DOI： 10.3788/YJYXS20163105.0477.

TANG Ling, CHEN Ming-ju,. Adaptive directional total variation denoising model based on image local direction[J]. , 2016,31(5): 477-483 DOI： 10.3788/YJYXS20163105.0477.

摘要

针对全变分模型(total variation

TV)以图像的梯度信息作为去噪的尺度参数

未考虑图像局部纹理的方向性的缺点

提出了一种基于图像局部方向特性的自适应全变分去噪模型(Adaptive directional total variation

ADTV)

并推导出该模型的迭代数值求解过程。在该模型中

首先

计算出图像局部方向的角度矩阵。然后

构造与图像纹理方向一致的椭圆区域代替TV模型的圆形区域。最后

通过优化最小化算法迭代求解以获得去噪后图像。通过对比实验证明

本文提出的模型取得了更高的峰值信噪比

去噪过程中更好地增强了图像的细节信息。

Abstract

In order to overcome the disadvantages that total variation model based on the image gradient information can't consider image's local direction and remove noise effectively

an adaptive directional total variation model based on image's local direction is proposed

and the iterative procedure of model is deduced. First

the image local direction is calculated in this new model. Second

an ellipse coinciding with the dominant direction of the local image is used to replace the unit ball in the total variation model. Finally

the denoised image is obtained by using iteration to optimize the minimum total variation. The comparing experimental results show that the proposed model can get higher peak signal to noise ratio and enhance the image details

and it has good performance in image denoising.

关键词

Keywords

references

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