In order to further improve the quality of image Super-Resolution (SR) reconstruction, a SR reconstruction algorithm based on Fields of Experts (FoE) prior model was proposed for the noise problem of reconstructed image in Non-locally Centralized Sparse Representation (NCSR) algorithm. Firstly, the FoE model was used to learn the prior knowledge of the whole image from the image training data to establish the global prior model, and then the learned prior information was used to solve the optimal sparse representation coefficient in the NCSR framework. Finally, the high resolution image estimate was obtainon. The proposed algorithm updated parameters of FoE prior model while the SR reconstruction iterative operates. Therefore, the effect of image reconstruction can be effectively enhanced by selecting the appropriate prior constraints without significantly increasing the computational complexity. Compared with NCSR algorithm, the experimental results show that the proposed algorithm can obtain better peak signal to noise ratio results for both noiseless and noisy degradation images, and further improves the de-noising effect of noisy images.