A regularized restoration algorithm based on maximum-likelihood estimation was presented for restoring object images from the noisy turbulence-degraded images. The logarithmic maximum-likelihood function for multi-frame image data based on the model of image random field was built, and some auxiliary terms to smooth noise while preserve the edges of images and the penalized item to avoid trivial solutions were added to the maximum-likelihood function. The iterative formulas of calculating the PSFs and object image were derived so that the PSFs and the object image could be estimated in the iterative manner. A parallel processing scheme for the algorithm is also proposed.The restoration experiments on the simulated turbulence-degraded images in the case of noise show that the proposed algorithm has high ability of noise-resisting and it has some practical applications.