In order to restore turbulence-degraded images rapidly and exactly, an accelerated iterative blind deconvolution (IBD) Richardson-Lucy (R-L) algorithm based on Huber regularization is proposed. The Huber function can select L1 and L2 norms adaptively based on the processing result of image filter. In the smooth area, the Huber function becomes the usual L2 least-squares penalty function with rapid convergence characteristics which can remove false edge, and in the edge area, the L1 penalty function which can maintain the detail information and edge. Then the second-order vector extrapolation acceleration technique is used to accelerate convergence rate. The experimental results show that the proposed algorithm has greater convergence rate and better subjective and objective restoration quality.