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We propose a new method based on the accelerated expectation maximization (EM) algorithm to learn the unknown image parameters and restoration. Acceleration is provided using fisher scoring (FS) optimization in the M step. Only a small number FS iteration is required for each M step. Our proposed algorithm reaches to the local minima in few steps whereas conventional EM needs more iteration. We also estimate the regularization parameter in the same single structure. Thanks to the FS optimization, it is possible to avoid complicated second derivative of the log-likelihood function by using only the gradient values.