压缩感知CS——采用小波变换进行稀疏表示，高斯随机矩阵为观测矩阵，重构算法为ROMP算法，对256*256的lena图处理，比较原图和ROMP算法在不同采样比例(0.74、0.5、0.3)下的重构效果，并各运行50次，比较算法性能PSNR和每次的运行时间

-Compressed sensing CS- using wavelet transform as sparse representation, Gaussian random matrix as the observation matrix and ROMP algorithm as the reconstruction algorithm. Compare lena figure and the reconstruction results using ROMP algorithm at different sampling ratio (0.74,0.50.3),then each runs 50 times, compare the performance of PSNR and each running time