A Compression Sensing Sampling Scheme Based on the Golden Section Principle

In classic radial sampling scheme, angle intervals between adjacent straight sampling lines are the same. And so the coherence between the sampling matrix and the sparse matrix is high. Many pseudo-shadows will occur on the reconstructed image. For this problem, we put forward a novel sampling scheme in which the scan lines are placed in the manner of golden section angle interval. In the resulting sampling matrix, the scan lines scatter randomly on the whole. Experimental results demonstrate that the algorithm proposed in this paper has lower correlation coefficient between sampling matrix and sparse matrix than classic radial sampling scheme and random radial sampling scheme. Image can be accurately reconstructed by collecting less data than the other two schemes.