To solve the problems such as over-reliance on massive data and weak prior generalization ability in the training procedure of image super-resolution，thus further to improve the quality of reconstructed high resolution image，a new image super-resolution algorithm was proposed. This paper firstly extends the theory of image self-similarity and points out that the self-similarity of ideal reconstruction image is extremely strong，but this property can be sharply weakened when the reconstructed image is attacked with some degradation factors. Then this discovery is considered as a prior and described by constructing a joint Gaussian mixture model，so that the self-similarity of each reconstructed image patch in the prior term can be represented by a specific Gaussian distribution. For maintaining the training samples' consistency，only the image patches extracted in the input image closed to its spatial position are permitted to join in the modeling process for each high-resolution image patch. This style can avoid the step of finding the nearest neighbors which is liable to introduce errors. Finally，the whole high-resolution image can be reconstructed patch-wise in an iterative way. Extensive experiments demonstrate that the reconstructed images generated by the proposed algorithm are clear and natural，in which the salient edges and texture structures are effectively preserved，and the correct high-frequency information is recovered. The 3× super-resolution experiment in BSD500 shows that the average PSNR is higher 0.529 db than the state-of-the-art algorithm MMPM，and the average SSIM is 0.030 higher than MMPM.