In this paper, an iterative regularized super resolution (SR) algorithin considering non-Gaussian noise is proposed. Based on the assumption of a generalized Gaussian distribution for the contaminating noise, an lp norm is adopted to measure the data fidelity term in the cost function. In the meantime, a regularization functional defined in terms of the desired high resolution (HR) image is employed, which allows for the simultaneous determination of its value and the partly reconstructed image at each iteration step. The convergence is thoroughly studied. Simulation results show the effectiveness of the proposed algorithm as well as its superiority to conventional SR methods.