基于TVAL3算法不同测量矩阵对图像重构质量的影响

Influence of Different Measurement Matrix Based on TVAL3 Algorithm on Image Reconstruction Quality

压缩感知重构是指利用得到的随机测量值恢复原始信号的过程。由于信号是稀疏的或可压缩的,压缩感知问题的求解是寻求方程最稀疏解(即最少非零值)的过程。首先给出了六种测量矩阵的构造方法,之后介绍了一种高质量重建信号的重构方法TVAL3,给出了四个评价图像重构质量的参数,并在此基础上仿真了不同测量矩阵对图像重构质量的影响。

Compressive sensing reconstruction is the process of recovering the original signal using the obtained random measurement values. Because the signal is sparse or compressible, the solution of the compressed sensing problem is the process of finding the sparsest solution of the equation. At first, the construction method of six measurement matrices is given. And then, a reconstruction method TVAL3 of high quality reconstruction signal is introduced, and four assessment parameters of image reconstruction quality are given. At last, the influence of different measurement matrix on image reconstruction quality is simulated.