A robust iterative multiframe SRR based on Andrew's Sine stochastic estimation with Andrew;s Sine-Tikhonov regularization

Traditionally, the concept of Super Resolution Reconstruction (SRR) relates to a process whereby images are obtained with resolutions that are beyond the limiting factors of the uncompensated imaging system. Many such SRR algorithms have been proposed during this decade but almost SRR estimations are based on L1 or L2 statistical norm estimation therefore these SRR algorithms are usually very sensitive to their assumed model of data and noise 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 Andrew's Sine norm is proposed for measuring the difference between the projected estimate of the high-resolution image and each low resolution image, removing outliers in the data. Moreover, Tikhonov regularization and Andrew's Sine-Tikhonov regularization are proposed to remove artifacts from the final answer and improve the rate of convergence. A number of experimental results are presented to demonstrate the efficacy of the proposed algorithm in comparison to other super-resolution algorithms based on L1 and L2 norm for a several noise models such as noiseless, AWGN, Poisson, Salt & Pepper Noise and Speckle Noise.