In this paper, we develop a novel subspace-based recovery algorithm for non-blind deconvolution (named SND). With considering visual importance difference between image structures and smoothing areas, we propose subspace data fidelity for protecting image structures and suppressing both noise and artifacts. Meanwhile, with exploiting the difference of subspace priors, we put forward differentiation modelings on different subspace priors for improving deblurring performance. Then we utilize the least square integration method to fuse deblurred estimations and to compensate information loss of subspace deblurrings. In addition, we derive an efficient optimization scheme for addressing the proposed objective function by employing the methods of least square and fast Fourier transform. Final experimental results demonstrate that the proposed method outperforms several classical and state-of-the-art algorithms in both subjective and objective assessments.