Andrew’s sine estimation for a robust iterative multiframe Super-Resolution Reconstruction using stochastic regularization technique

In these two decades, although there has been a great deal of research developing super-resolution reconstruction (SRR) algorithms and many such algorithms have been proposed, the almost SRR algorithms are based on L1 or L2 statistical norm estimation. Consequently, these SRR algorithms are typically very sensitive to their assumed noise model that limits their utility. This paper proposes a novel SRR algorithm based on the stochastic regularization technique of Bayesian MAP estimation by minimizing a cost function. The Andrewpsilas Sine norm is used for measuring the difference between the projected estimate of the high-resolution image and each low resolution image and for removing outliers in the data. Moreover Tikhonov regularization is used to remove artifacts from the final answer and improve the rate of convergence. Finally, the efficiency of the proposed algorithm is demonstrated here in a number of experimental results using Lena standard images and using a several noise models such as noiseless, AWGN, Poisson noise and salt & pepper noise.