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A single frame image super-resolution reconstruction algorithm based on sparse representation and neighbor embedding was proposed.Two complete dictionaries based on sparse representation were trained for low and high resolution image patches,in which the closest image patches to the two dictionary atoms were chosen.The weight of reconstructed image patches was represented by image patches neighbor.Once weight matrix was gotten,High resolution image patch can be expressed as low resolution image patch multipling by the corresponding weight.Compared with previous algorithms,when calculating the distance between the dictionary atoms and image patches,the proposed algorithm is not each image patch to calculate.Instead image patches are clustered, and calculate the distance between dictionary atoms and the clustering center, then select image patches in the closest category.Calculated time of weight matrix can be greatly reduced, and improve the computational efficiency.The resulting PSNR compared with other algorithms, there are also improved obviously. 