A robust iterative multiframe SRR using stochastic regularization technique based on hampel estimation

Within the traditional research community in this decade, there has been recently a great deal of work developing Super-Resolution Reconstruction (SRR) algorithms for combining a set of low quality images to produce a set of higher quality images. While many such algorithms have been proposed, the almost SRR estimations are based on L1 or L2 statistical norm estimation. Hence, these algorithms are usually too sensitive to their assumed noise model limiting their application. In this paper, we propose a novel SRR framework using the stochastic regularization technique by minimizing a cost function. The Hampel norm is used for measuring the difference between the projected estimate of the high quality image and each low high quality image and for removing outliers in the data. To remove artifacts from the final answer and improve the rate of convergence, Tikhonov regularization is incorporated in the proposed framework. Finally, a number of experimental results are presented to demonstrate the performance of the proposed algorithm in comparison to several previously published methods using a several noise models such as noiseless, AWGN, Poisson noise and Salt & Pepper noise.