利用概率对稀疏域划分的压缩感知方法

doi:10.3969/j.issn.1001-2400.2013.06.009

Abstract

Abstract:

With the development of Compressive Sensing theory in recent years, many new algorithms have been introduced to this field. But still, these algorithms tend to judge the probability of the nonzero signal in each position of the sparse domain as the same, which is in fact not true. In this topic we discuss orthogonal coefficient distribution and divide the whole sparse domain into different parts using probability. With the method called Sparse domain Division using Probability (SDP), the reconstructed speed would increase 20～60 times without producing any negative effect on image quality at the same sampling rate.

Key words: orthogonal coefficient distribution, sparse domain division using probability, compressive sensing

TIAN Yumin;SONG Jun2. Compressive sensing with sparse domain division using probability[J].J4, 2013, 40(6): 52-57.

References

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Compressive sensing with sparse domain division using probability

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