通信学报 ›› 2016, Vol. 37 ›› Issue (1): 100-109.doi: 10.11959/j.issn.1000-436x.2016011

• 学术论文 • 上一篇    下一篇

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   

  1. 福州大学物理与信息工程学院,福建 福州 350116
  • 出版日期:2016-01-25 发布日期:2016-01-27
  • 基金资助:
    国家自然科学基金资助项目;国家自然科学基金资助项目;福建省自然科学基金资助项目;福建省自然科学基金资助项目;福建省自然科学基金资助项目;福建省科技重大专项基金资助项目;福建省教育厅基金资助项目

Reconstruction algorithm for block compressed sensing based on variation model

Jian CHEN,xiong SUKai,zhi YANGXiu,kui ZHENGMing,qun LINLi   

  1. College of Physics and Information Engineering, Fuzhou iversity, Fuzhou 350116, China
  • Online:2016-01-25 Published:2016-01-27
  • Supported by:
    The National Natural Science Foundation of China;The National Natural Science Foundation of China;The Natural Science Founda-tion of Fujian Province;The Natural Science Founda-tion of Fujian Province;The Natural Science Founda-tion of Fujian Province;The Major Technology Project of Fujian Prov-ince;The Education Department Project of Fujian Province

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。

关键词: 全变分, 图像重构, 块压缩感知, 交替方向乘子法

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.

Key words: total variation, image reconstruction, block compressed sensing, alternating direction method of multipliers

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