基于变分模型的块压缩感知重构算法

陈建; 苏凯雄; 杨秀芝; 郑明魁; 林丽群

doi:10.11959/j.issn.1000-436x.2016011

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基于变分模型的块压缩感知重构算法

学术论文 | 更新时间：2024-06-05

- 基于变分模型的块压缩感知重构算法
- Reconstruction algorithm for block compressed sensing based on variation model
- 通信学报 2016年37卷第1期页码：100-109
- 作者机构：
  
  福州大学物理与信息工程学院，福建福州 350116
- 作者简介：
  
  [ "陈建（1981-），女，福建福州人，福州大学讲师，主要研究方向为视频编解码、压缩感知。" ]
  [ "苏凯雄（1959-），男，福建罗源人，福州大学教授、博士生导师，主要研究方向为多媒体通信、数字电视广播。" ]
  [ "杨秀芝（1963-），女，山西灵石人，福州大学教授、硕士生导师，主要研究方向为图像处理、数字电视技术。" ]
  [ "郑明魁（1976-），男，福建闽侯人，福州大学副教授，主要研究方向为多媒体视频编码。" ]
  [ "林丽群（1980-），女，福建莆田人，福州大学讲师、博士生，主要研究方向为图像处理。" ]
- 基金信息：
  
  国家自然科学基金资助项目(61471124);国家自然科学基金资助项目(61571129);福建省自然科学基金资助项目(2013J01234);福建省自然科学基金资助项目(2014J01234);福建省自然科学基金资助项目(2015J01251);福建省科技重大专项基金资助项目(2014HZ0003-3);福建省教育厅基金资助项目(JA14065)
- DOI：10.11959/j.issn.1000-436x.2016011
  中图分类号： TN911.73
- 网络出版日期：2016-01，
  
  纸质出版日期：2016-01-25
- 稿件说明：
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陈建, 苏凯雄, 杨秀芝, 等. 基于变分模型的块压缩感知重构算法[J]. 通信学报, 2016,37(1):100-109.

Jian CHEN, xiong SUKai, zhi YANGXiu, et al. Reconstruction algorithm for block compressed sensing based on variation model[J]. Journal on communications, 2016, 37(1): 100-109.
陈建, 苏凯雄, 杨秀芝, 等. 基于变分模型的块压缩感知重构算法[J]. 通信学报, 2016,37(1):100-109. DOI： 10.11959/j.issn.1000-436x.2016011.

Jian CHEN, xiong SUKai, zhi YANGXiu, et al. Reconstruction algorithm for block compressed sensing based on variation model[J]. Journal on communications, 2016, 37(1): 100-109. DOI： 10.11959/j.issn.1000-436x.2016011.

摘要

为了提高现有块压缩感知重构算法的性能，提出了基于全变分和混合变分模型的块压缩感知（简称BCS-TV和BCS-MV）算法。该方法以块为单位进行图像采样，以自然图像正则项的稀疏性为先验条件，通过变型的增广拉格朗日交替方向乘子法(ALM-ADMM)，在整幅图像范围内逼近目标函数来重构原始图像。与以前基于一致性块采样的压缩感知工作对比，该算法的PSNR约提高1.5 dB

SSIM约提高0.05，运行速度较稳定，特别适合具有固定传输时延的多媒体数据处理场合。

Abstract

The algorithms for block compressed sensing based on total variation and mixed variation (abbreviated as BCS-TV and BCS-MV) models were proposed to improve the performance of current reconstruction algorithms for the block-based compressed sensing. In the measuring phase

an image was sampled block-by-block. In the recovering period

it took the sparse regularization of the natural image as a priori knowledge

and approached the target function within the whole image through the modified augmented Lagrange method and alternating direction method of multipliers (ALM-ADMM). The method proposed achieves average PSNR gain of 1.5 dB and SSIM gain of 0.05 at a more stable running speed

over the previous uniformly block-based compressed sensing. It is particularly suitable for the applications of the multimedia data processing with fixed transmission delay.

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Keywords

references

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