A multiplicative iterative blind image deconvolution algorithm was proposed, in which both estimated image and Point Spread Function (PSF) were assumed to follow Markov Random Field (MRF) models. In addition to natural preservation of non-negative constraints, the algorithm introduced a set of novel antinoise difference operators, which first carried out weighted summation and then difference. Alternating Minimization (AM) method was adopted to linearly decompose the blind image deconvolution that belonged to a class of nonlinear inverse problems into alternate image restoration step and PSF estimation step. During the alternate iteration, an acceleration method called as vector extrapolation was applied to the alternate iteration steps owing to slow convergence of the algorithm. Numerical experiment results show the proposed algorithm can rapidly realize the restoration of the degraded High Resolution (HR) image from China & Brazil earth resource satellite-02B (CBERS-02B) meanwhile effectively preserves detailed edges and inhibits noise.