Ghost imaging system requires a large number of samples to reconstruct the object. Computational ghost imaging can use well-designed pre-modulated orthogonal patterns to reduce the requirement of sampling number and increase the imaging quality, while the rotating ground glass (RGG) scheme cannot. Instead of the pre-modulation method, a post-processing method using Gram-Schmidt process to orthonormalize the patterns in a RGG scheme is introduced. Reconstructed ghost image after the Gram-Schmidt process (SGI) are tested using the quality indicators such as the Contrast-to-Noise (CNR), the Peak Signal to Noise Ratio (PSNR), the Correlation Coefficient (CC) and reducing the Mean Square Error (MSE). Simulation results show that this method has obvious advantage on enhancing the efficiency of image acquisition, and the sampling number requirement drops from several thousands to a few hundreds in ideal condition. However, in actual system with noise, the image quality from SGI declines in large sampling number, for noise and errors accumulate in the orthonormalization process. So an improved Group SGI method is then developed to avoid this error accumulation, which behaves effectively in reconstructing the image from experimental data and show good performances in large sampling number too. Since this method do not change the relationship between the reference patterns and the bucket values, it can easily combine with most of reconstruction algorithms and improve their reconstruction efficiency.