In recent years Compressed Sensing (CS) has drawn quite an amount of attention as novel digital signal sampling theory when the signal is sparse in some domain. However, signal recovery from compressed measurement data has always been challenging due to its implicit ill-posed nature. In this paper we will propose an improved CS image recovery algorithm by intra prediction method based on block-based CS image framework. The current block is firstly predicted by its neighbor pixels, and then its prediction residual is recovered. The performance of our proposed CS image recovery algorithm is superior to the traditional CS recovery algorithm because the sparsity level of prediction residual is higher than its original image block. Experimental results demonstrate that the proposed algorithm outperforms the traditional CS image recovery algorithm and achieve by far 2dB gain in PSNR.