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.