A robust iterative multiframe SRR based on Hampel stochastic estimation with Hampel-Tikhonov regularization

Abstract

Typically, super resolution reconstruction (SRR) is the process by which additional information is incorporated to enhance a noisy low resolution image hence producing a high resolution image. Although many such SRR algorithms have been proposed, almost SRR estimations are based on L1 or L2 statistical norm estimation hence 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 Hampel 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 and Hampel-Tikhonov regularization are 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 algorithms based on L1 and L2 norm for a several noise models such as noiseless, AWGN, Poisson, Salt & Pepper Noise and Speckle Noise.