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The success of SRR algorithms is highly dependent on the accuracy of the model of the imaging process. When the data or noise model assumptions do not faithfully describe the measure data, the estimator performance degrades. Most noise models used in SRR algorithms are based on AWGN model at low power therefore SRR algorithms can effectively apply only on the image sequence that is corrupted by AWGN. The real noise models that corrupt the measure sequence are unknown; consequently, SRR algorithm using L1 or L2 norm may degrade the image sequence rather than enhance it. Therefore, the robust norm applicable to several noise and data models is desired in SRR algorithms. This paper proposes an alternate SRR approach based on the stochastic regularization technique of Bayesian MAP estimation by minimizing a cost function. The Tukey norm is used for measuring the difference between the projected estimate of the high-resolution image and each low resolution image, removing outliers in the data and Tikhonov regularization is used to remove artifacts from the final answer and improve the rate of convergence. Due to real sequences and complex motion sequences, the fast affine block-based registration is used for registration step of SRR. The experimental results show that the proposed SRR can apply on real sequence such as Suzie sequence and confirm the effectiveness of our method and demonstrate its superiority to other super-resolution methods based on L1 and L2 norm for a several noise models such as AWGN, Poisson and Salt & Pepper Noise.