根据稀疏基底选择抽样模型

在压缩传感技术应用中,根据稀疏基底选择抽样模型对重构结果影响很大。在傅里叶空间中,极坐标星形抽样和随机抽样的重构效果差异巨大,应用傅里叶光学理论对傅里叶空间的频谱分布进行分析,从理论上解释了原因,并且据此提出稀疏基底和抽样模型的匹配情况会影响重构效果。在小波空间中,进行了均匀抽样和随机抽样的对比重构实验,发现后者的重构效果更好,并确定了根据稀疏基底选择合适抽样模型的可行性,为实际应用中降低抽样率,提高重构效果提供了方法依据。

Sampling Model Selection According to Sparse Base

The study about the compressed sensing technology indicates that selecting sampling model according to the sparse base has great influence on the reconstruction results. In the Fourier space, there are huge differences between the reconstructions of the stellate sampling of polar coordinates and the random sampling. The spectral distribution of the Fourier space is analyzed by Fourier optical theory and the phenomena is explained theoretically. It is proposed that the matching degree between the sparse base and the sampling model may affect the reconstruction effect. In the wavelet space, a comparison experiment of reconstruction is taken with the uniform sampling and the random sampling, and the latter is better. Therefore, a method is provided for the practical application to reduce the sampling rate and improve the reconstruction effect by choosing suitable sampling models according to the sparse bases.