Variation model with group sparsity constraint for stripe noise removal

Liu CHEN; Ming-ju CHEN; Hao WU; Zhi-shuang XUE

doi:10.3788/YJYXS20203506.0604

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Variation model with group sparsity constraint for stripe noise removal

Image Processing | 更新时间：2020-07-13

- Variation model with group sparsity constraint for stripe noise removal
- Chinese Journal of Liquid Crystals and Displays Vol. 35, Issue 6, Pages: 604-611(2020)
- 作者机构：
  
  1.四川轻化工大学人工智能四川省重点实验室, 四川自贡 643000
  2.西南科技大学特殊环境机器人技术四川省重点实验室, 四川绵阳 621000
- 作者简介：
- 基金信息：
  
  Project of Sichuan Department of Science and Technology(2018GZDZX0043);Project of Sichuan Department of Science and Technology(2019YJ0476);Project of Sichuan Department of Science and Technology(2019YJ0477);Project of Enterprise Informatization and Internet of Things Measurement Key Laboratory of Sichuan Education Department(2018WZY01)
- DOI：10.3788/YJYXS20203506.0604
  CLC： TP391.41
- Received：25 October 2019，
  
  Revised：20 December 2019，
  
  Accepted：20 December 2019，
  
  Published：05 June 2020
- 稿件说明：
移动端阅览
Liu CHEN, Ming-ju CHEN, Hao WU, et al. Variation model with group sparsity constraint for stripe noise removal[J]. Chinese journal of liquid crystals and displays, 2020, 35(6): 604-611.
DOI：

Liu CHEN, Ming-ju CHEN, Hao WU, et al. Variation model with group sparsity constraint for stripe noise removal[J]. Chinese journal of liquid crystals and displays, 2020, 35(6): 604-611. DOI： 10.3788/YJYXS20203506.0604.

摘要

在对图像变分描述的前提下，为有效地利用条带噪声之间的相似性，本文将条带噪声的群稀疏表示引入到单向变分模型中，提出群稀疏技术限制的单向变分模型，并采用交替方向乘子法求解该模型。对比实验证明，本文所提出的群稀疏限制的单向变分模型能有效地利用条带噪声的相似性实现条带噪声的消除，更好地重构图像的细节信息，峰值信噪比与结构相似性比其他模型分别提高6.76 dB和0.25，图像去噪性能更优。

Abstract

By introducing the group sparse representation of strip noise into the single variation model

a single variational model restricted by group sparse technique is proposed to remove noise. The proposed model is solved by alternating direction method of multipliers. The experiments show that the proposed model can more effectively use the similarities between strip noise to remove noise compared with other existing models

and it can reconstruct the image information better. The values of peak signal-to-noise ratio and structural similarity are increased by 6.76 dB and 0.25

respectively

which shows good image denoising performance of our model.

关键词

Keywords

references

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