基于自适应非正交稀疏化变换学习过完备字典的图像稀疏表示

Zunyi Tang, Zuyuan Yang, Shuxue Ding
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引用次数: 2

摘要

如何学习图像稀疏表示的过完备字典是机器学习、稀疏编码、盲源分离等领域的重要课题。所谓的k奇异值分解(K-SVD)方法[3]对于这一目的是强大的,但是,太费时的应用。近年来,人们提出了一种快速学习字典的自适应正交稀疏化变换(AOST)方法。但是,相应的系数矩阵可能没有K-SVD那么稀疏。为了解决这一问题,本文提出了一种非正交迭代匹配的字典学习方法。采用顺序提取堆叠图像块列的方法,自适应学习字典的非正交原子,得到的系数矩阵更稀疏。实验结果表明,该方法可以生成有效的字典,并且生成的图像表示比ast更稀疏。
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Sparse Representations of Image via Overcomplete Dictionary Learned by Adaptive Non-orthogonal Sparsifying Transform
How to learn an over complete dictionary for sparse representations of image is an important topic in machine learning, sparse coding, blind source separation, etc. The so-called K-singular value decomposition (K-SVD) method [3] is powerful for this purpose, however, it is too time-consuming to apply. Recently, an adaptive orthogonal sparsifying transform (AOST) method has been developed to learn the dictionary that is faster. However, the corresponding coefficient matrix may not be as sparse as that of K-SVD. For solving this problem, in this paper, a non-orthogonal iterative match method is proposed to learn the dictionary. By using the approach of sequentially extracting columns of the stacked image blocks, the non-orthogonal atoms of the dictionary are learned adaptively, and the resultant coefficient matrix is sparser. Experiment results show that the proposed method can yield effective dictionaries and the resulting image representation is sparser than AOST.
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