In this paper, a blind image restoration algorithm based on reweighted graph total variation combined with hyper-Laplacian is proposed. First, the bimodal distribution of the weight of a blurred image is reconstructed using the reweighted graph total variation. Next, the reconstructed image is used to estimate the continuity and sparsity of the point spread function (PSF) and the blurred image is restored by the PSF. These two processes are repeatedly iterated to make the PSF approach the ideal solution continuously. Finally, we combined it with a priori, that is, the hyper-Laplacian cave, which can best fit a natural image gradient distribution to achieve the non-blind restoration of the blurred image. Experimental results show that the proposed algorithm can give a more accurate prediction of the blurred kernel and effectively reduce the ringing effect in images compared with two representative blind restoration algorithms developed in recent years. Moreover, there is an improvement in subjective vision and objective elevation indicators.