Abstract: To solve the unstable generic image reconstruction problem,an improved image super resolution algorithm was presented for exploiting the properties of sparse representation and matrix completion.Over-complete dictionary pair was established by training natural images.According to the local prior constraints,sparse coefficients of each low resolution image patches would be estimated.In multi-frame images,the sparse coefficients of each frame were similar.Therefore,the inexact augmented Lagrange multiplier method was adopted to achieve matrix completion and recovery in the process of recurring global constraints.The final high resolution image would be generated from output low-rank matrix.Resultsshow that the methods are effective in retaining the image edges and details,and it is of robustness to the scope of dictionary with higher PSNR value.This algorithm can be applied in remote sensing image super-resolution reconstruction areas.