The parameter of traditional regularized Super Resolution (SR) reconstruction model is difficult to choose:the higher parameter value results in blurred reconstruction and the fading of edge and detail, while the lower parameter value weakens the denosing ability. A double regularization parameters super resolution reconstruction method based on structure tensor was proposed. Firstly, smooth region and edge was detected by using local structure tensor. Secondly, the Total Variation (TV) was weighted with the priori information of difference curvature. Finally, two different parameters toward smooth region and edge were used to reconstruct super resolution image. The experimental data show that the proposed algorithm can improve the Peak Signal-to-Noise Ratio (PSNR) of 0.033-0.11 dB, and get better reconstruction results. The proposed algorithm can effectively improve the reconstruction effect of Low Resolution (LR) video frames, and can be applied to LR video enhancement, license plate recognition and the interest target enhancement in video surveillance, etc.