Multilevel dictionary learning for sparse representation of images
Multilevel dictionary learning for sparse representation of images
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DOI：
10.1109/dsp-spe.2011.5739224
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发表时间：
2011-01
期刊：
影响因子：
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通讯作者：
Jayaraman J. Thiagarajan;K. Ramamurthy;A. Spanias
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文献类型：
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作者：
Jayaraman J. Thiagarajan;K. Ramamurthy;A. Spanias
与涉及数据表示和分类的应用中的预定义字典相比，用于稀疏近似的自适应数据驱动字典提供了上级性能。在本文中，我们提出了一种新的算法学习全球字典特别适合于稀疏表示的自然图像。该算法使用一个层次的基于能量的学习方法来学习一个多级字典。在第一级中学习对表示贡献最大能量的原子，而在后续级别中学习贡献较小能量的原子。将学习的多级字典与使用K-SVD算法学习的字典进行比较。使用少量的非零系数的重建结果表明，利用能量层次使用多级字典的优势，指出在低比特率图像压缩的潜在应用。在压缩感知中使用优化的传感矩阵与少量的测量的上级性能也被证明。
Adaptive data-driven dictionaries for sparse approximations provide superior performance compared to predefined dictionaries in applications involving representation and classification of data. In this paper, we propose a novel algorithm for learning global dictionaries particularly suited to the sparse representation of natural images. The proposed algorithm uses a hierarchical energy based learning approach to learn a multilevel dictionary. The atoms that contribute the most energy to the representation are learned in the first level and those that contribute lesser energies are learned in the subsequent levels. The learned multilevel dictionary is compared to a dictionary learned using the K-SVD algorithm. Reconstruction results using a small number of non-zero coefficients demonstrate the advantage of exploiting energy hierarchy using multilevel dictionaries, pointing to potential applications in low bit-rate image compression. Superior performance in compressed sensing using optimized sensing matrices with small number of measurements is also demonstrated.