A Robust Iterative Multiframe Super-Resolution Reconstruction using a Huber Bayesian Approach with Huber-Tikhonov Regularization

The traditional SRR (super-resolution reconstruction) estimations are based on L1 or L2 statistical norm estimation therefore 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 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 Huber 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 and Huber-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 methods and demonstrate its superiority to other super-resolution methods based on L1 and L2 norm for several noise models such as noiseless, AWGN, Poisson and salt & pepper noise