| 摘要: |
| 针对分块压缩感知算法在平滑块效应时损失了大量的细节纹理信息，从而影响图像的重构效果问题，提出了一种基于块稀疏信号的压缩感知重构算法。该算法先采用块稀疏度估计对信号的稀疏性做初步估计，通过对块稀疏度进行估算初始化阶段长，运用块矩阵与残差信号最匹配原则来选取支撑块，再运用自适应迭代计算实现对块稀疏信号的重构，较好地解决了浪费存储资源和计算量大的问题。实验结果表明，相比常用压缩感知方法，所提算法能明显减少运算时间，且能有效提高图像重构效果。 |
| 关键词: 图像处理 压缩感知 信号重构 块稀疏 稀疏度估计 |
| DOI： |
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| 基金项目:国家自然科学基金青年科学基金（41701479）；黑龙江省科学基金项目（QC2018045）；中国博士后科学基金项目（2017M621246）;黑龙江省博士后科学基金项目（LBH-Z17052）；黑龙江省省属高等学校基本科研业务费科研项目（135309342）；2018年国家级大学生创新创业训练计划资助项目（201810232018） |
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| A compressed sensing method based on block sparsity and adaptive iteration |
| SHI Cuiping,LIU Huanhuan |
| (College of Communication and Electronic Engineering,Qiqihar University,Qiqihar 161006,China) |
| Abstract: |
| For the problem that block compressed sensing algorithm loses a lot of detail texture information when smoothing block effect,which affects the image reconstruction effect,a compressed sensing reconstruction algorithm based on block sparse signal is proposed.Firstly,the sparsity of the signal is estimated by block sparsity estimation method,and then the step size is initialized.The support block is selected by using the principle of the best match between block matrix and residual signal.Then,the adaptive iteration calculation is performed to reconstruct the sparse signal,which solves the problem of wasting storage resources and large amount of computation.The experimental results show that the proposed algorithm can significantly reduce the computational time and effectively improve the image reconstruction effect compared with the commonly used compressed sensing methods. |
| Key words: image processing compressed sensing signal reconstruction block sparse sparsity estimation |
