压缩感知图像重构中矩阵互相关性的研究

Study of correlation between matrix in compressed sensing image reconstruction

从测量矩阵和稀疏矩阵的互相关性角度出发,通过对测量矩阵和稀疏矩阵所构成的Gram矩阵进行门限选择,进而经过相应的缩放处理降低互相关性,这样不仅可以获取更多有信息量的测量值,而且可以完成对测量矩阵的优化改进。通过在DWT、DCT下的压缩感知图像重构实验验证了该方法的可行性,恢复效果得到一定程度的提高,相比于传统的小波恢复重构,达到了预期的效果。

This paper begins with the perspective of the correlation between measurement matrix and sparse matrix. By making threshold selections of Gram matrix constituted by measurement matrix and sparse matrix, and making appropriate scaling processing, the conrelation is reduced. It can not only obtain more measured value with information content, but also finish the work to improve the measurement matrix. Through the DWT and DCT compressed sensing image reconstruction experiments, the feasibility of the method is verified and the recovery results have been improved to some extent. Compared with the conventional wavelet restoration reconstruction, it achieves the desired results.