Image compressive sensing recovery based on weighted structure group sparse representation

Jia LI; Zhi-rong GAO; Cheng-yi XIONG; Cheng ZHOU

doi:10.11959/j.issn.1000-436x.2017041

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Image compressive sensing recovery based on weighted structure group sparse representation

Correspondences | 更新时间：2024-06-05

- Image compressive sensing recovery based on weighted structure group sparse representation
- Journal on Communications Vol. 38, Issue 2, Pages: 196-202(2017)
- 作者机构：
  
  1. 中南民族大学电子信息工程学院智能无线通信湖北省重点实验室，湖北武汉 430074
  2. 中南民族大学计算机科学学院，湖北武汉 430074
- 作者简介：
- 基金信息：
  
  The National Natural Science Foundation of China(61471400)
- DOI：10.11959/j.issn.1000-436x.2017041
  CLC： TN911.72
- Online First：2017-02，
  
  Published：25 February 2017
- 稿件说明：
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Jia LI, Zhi-rong GAO, Cheng-yi XIONG, et al. Image compressive sensing recovery based on weighted structure group sparse representation[J]. Journal on Communications, 2017, 38(2): 196-202.
DOI：

Jia LI, Zhi-rong GAO, Cheng-yi XIONG, et al. Image compressive sensing recovery based on weighted structure group sparse representation[J]. Journal on Communications, 2017, 38(2): 196-202. DOI： 10.11959/j.issn.1000-436x.2017041.

摘要

利用图像的非局部相似性先验以提升图像恢复质量已得到广泛关注。为了更有效地提升压缩感知（CS）图像的重构质量，提出了一种基于加权结构组稀疏表示（WSGSR）的图像压缩感知重构方法。采用非局部相似图像块结构组加权稀疏表示的

范数作为规则化项约束优化重构，实现在更好地恢复图像高频细节信息的同时有效减少对图像低频成分的损失，图像重构质量得到明显改善。推导出一种加权软阈值收缩方法，实现对模型的优化求解，对幅值较大的重要系数采用较小的阈值收缩处理，对幅值较小的非重要系数采用相对较大的阈值收缩处理。实验结果比较验证了所提方法的有效性。

Abstract

Non-local similarity prior has been widely paid attention to efficiently improve image recovery quality.To fur-ther improve the recovered image quality for compressive sensing (CS)

an image compressive sensing recovery method based on reweighted structure group sparse representation (WSGSR) was proposed.

-norm of WSGSR of image non-local similar patch group was used as a regularization term to optimize reconstruction

which achieved well reserving image high-frequency detail with less loss of image low-frequency component

and thus considerably improve the recon-structed image quality.A reweighted soft thresholding shrinkage method was deduced to achieve optimization solution

in which the significant coefficient with large magnitude value was shrunk by a small threshold

while the non-significant coefficient with small magnitude value was shrunk by a relative large threshold.Experimental results comparison demon-strate the effectiveness of the proposed method.

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references

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