To solve the problems of large computation cost, ringing and noise sensitivity in blind restoration algorithms for microscopic images, the blind restoration algorithm under Bayesian framework based on two guided filterings is proposed. The depth information of microscopic image is used to estimate the probabilistic model of point spread function, and a minimum optimization problem under the Bayesian framework is built. The guided filtering is applied to searching the optimal solution through analyzing the solving scheme of the minimum optimization problem of the maximum posterior probability. The solution scheme of the two guided filtering algorithms is designed for removing ringing and noise, which means the restoration result of the first guided filtering will serve as input of the optimization problem again. Experimental results show that the pixel error rate of recovery result is around 0.04, which increases by 20% compared to those of other commonly used algorithms, and the running time is significantly shortened. The proposed algorithm can be used in assembly of the micro-structures for defocused image blind restoration.