For the motion blur problem of a visual image produced in moving processing, a blind image restoration method based on sparse representation and Weber's law is proposed. The method uses a shock filter to predict the sharp edges of blurred images, and a multi-scale strategy to estimate the blur kernel from a coarse estimation to a fine one. The sparse representation is treated as a priori knowledge for regularization constraint of blind image restoration model, and the Weber's law which reflects the human visual characteristics is combined to conduct blind restoration for the synthetic blurred image and the real blurred image. Experimental results show that the proposed method achieves better restoration results both for the performance indexes and the image textures. As compared with the Rob Fergus's method and Xu Li's method developed in recent years, it shows that the structural similarity (SSIM) is 0.762 4 and the Peak Signal to Noise Ratio (PSNR) is improved by 1.82 dB to 2.99 dB for the deblurred Lena image, and the SSIM is 0.858 9; and the PSNR has improved by 2.46 dB to 5.58 dB for the deblurred Cameraman image. Moreover, the proposed method reduces the boundary artifacts of the restored image, which is better consistent with human visual perception characteristics.