Reconstruction of compressive sensing and semi-QR factorization
可压缩传感重构算法与近似QR分解

In this paper, the signal reconstruction algorithms of Compressive Sensing (CS) were discussed and a new method to enhance the efficiency was found, and the quality of recovered images was improved: proximate QR factorization of measurement matrix. The exact reconstruction of minimum l0 norm is NP-complete problem. Minimum l1 norm reconstruction can approximate compressible vectors with high probability. In the study, the quality of solutions of l1 optimization can be enhanced further by changing the singular values of the measurement matrix with QR factorization. We illustrated the effectiveness of QR factorization of the measurement matrix and gave a comparison of the Gaussian random matrix and its QR factorization.