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文件名称:图像稀疏表示文献
文件大小:287KB
文件格式:PDF
更新时间:2015-05-03 09:41:21
图像稀疏分解
In this paper, we present a multi-scale dictionary
learning paradigm for sparse and redundant signal representa-
tions. The appeal of such a dictionary is obvious—in many cases
data naturally comes at different scales. A multi-scale dictionary
should be able to combine the advantages of generic multi-scale
representations (such as Wavelets), with the power of learned
dictionaries, in capturing the intrinsic characteristics of a family
of signals. Using such a dictionary would allow representing the
data in a more efficient, i.e., sparse, manner, allowing applications
to take a more global look at the signal. In this paper, we aim
to achieve this goal without incurring the costs of an explicit
dictionary with large atoms. The K-SVD usingWavelets approach
presented here applies dictionary learning in the analysis domain
of a fixed multi-scale operator. This way, sub-dictionaries at
different data scales, consisting of small atoms, are trained. These
dictionaries can then be efficiently used in sparse coding for
various image processing applications, potentially outperforming
both single-scale trained dictionaries and multi-scale analytic
ones. In this paper, we demonstrate this construction and discuss
its potential through several experiments performed on fingerprint
and coastal scenery images.
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