A Lorentzian Stochastic Estimation for a Robust and Iterative Multiframe Super-Resolution Reconstruction

In the last decades, it has seen a great deal of work in the development of algorithms addressing the problem of super-resolution. Although many such algorithms have been proposed, the almost SRR (super-resolution reconstruction) estimations are based on L1 or L2 statistical norm estimation thus these SRR methods are usually very sensitive to their assumed model of data and noise that limits their utility. This paper reviews some of these SRR estimator methods and addresses their shortcomings. We propose a novel SRR approach based on the stochastic regularization technique of Bayesian MAP estimation by minimizing a cost function. The Lorentzian 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. The experimental results 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 noiseless, additive white Gaussian noise (AWGN) and salt & pepper noise