| 引用本文: | 梁锐华,成礼智.基于小波域字典学习方法的图像双重稀疏表示.[J].国防科技大学学报,2012,34(4):126-131.[点击复制] |
| LIANG Ruihua,CHENG Lizhi.Double sparse image representation via learning dictionaries in wavelet domain[J].Journal of National University of Defense Technology,2012,34(4):126-131[点击复制] |
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| 基于小波域字典学习方法的图像双重稀疏表示 |
| 梁锐华, 成礼智 |
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(国防科技大学 理学院，湖南 长沙 410073)
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| 摘要: |
| 提出了一种有效地结构化字典生成算法以及图像双重稀疏表示方法。在Rubinstein等提出的图像双重稀疏表示模型的基础上，引入小波零树结构，将同一空间位置对应的同方向跨尺度小波基函数的线性组合作为新的基函数，并通过K-SVD学习算法得到线性组合系数，由此得到了一种更加切合图像方向特征的结构化字典学习算法。在此基础上提出了相应的图像分解与重构算法。遥感图像M项逼近实验以及压缩仿真实验表明，本文提出的结构化字典比已有的字典具有更好的图像稀疏表示效果。 |
| 关键词: 稀疏表示 字典学习 小波 零树 图像压缩 |
| DOI： |
| 投稿日期：2011-08-25 |
| 基金项目:国家自然科学基金资助项目（61072118） |
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| Double sparse image representation via learning dictionaries in wavelet domain |
| LIANG Ruihua, CHENG Lizhi |
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(College of Science, National University of Defense Technology, Changsha 410073, China)
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| Abstract: |
| A novel structured dictionary training algorithm is proposed for double sparse image representation. Based on the double sparse image representation model proposed by Rubinstein, the zero-tree structure of wavelet coefficients was introduced, and the new dictionary atoms were constructed by linear combination of wavelet bases in all high-frequency bands of same orientation across different scales. The linear combination coefficients were learned via K-SVD. The image decomposition and reconstruction algorithm was proposed based on the learned dictionary. The M-term approximation and compression of remote sensing images both proved the better effects of the proposed structured dictionary than the existing dictionaries. |
| Keywords: sparse representation dictionary learning wavelet zero-tree image compression |
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