To overcome disadvantages of the original Non-negativity and Support constraint Recursive Inverse Filtering (NAS-RIF) algorithm, an adaptive algorithm for the blind image restoration based on NAS-RIF algorithm was proposed. Firstly, regularization terms and space weights were added to the cost function of the original NAS-RIF algorithm. Through adaptively modulating the regularization parameters and space weights, not only the noise resistance ability could be improved, but the restored image could be smoothed. Then, image segmentation technique was employed in each iteration to find the precise object support region, meanwhile, the non-uniform background was replaced by the average background. Finally, the N-step-restart conjugate gradient routine was applied to optimization of the cost function, and then the convergence rate was enhanced. The experiments on degraded images derived from two kinds of blur operators were performed under different SNR (Signal Noise Ratio) conditions, and the ΔSNRs by proposed algorithm are 6.315 3 dB and 8.910 6 dB, respectively. The experiment results demonstrate that the proposed algorithm has a positive improvement in both reducing noises and preserving edges. Particularly, the proposed algorithm can obtain a better restoration result under a low SNR condition.