Video enhancement using a robust iterative SRR based on Hampel stochastic estimation

Many image video enhancements assume that the noise model of the imaging system is known in advance such as AWGN. However, the real noise model is not known in many practical applications. In this paper, we propose a novel robust video enhancement algorithm using SRR (super-resolution reconstruction) based on the stochastic regularization technique by minimizing a cost function. First, the classical registration process is used to estimate the relationship between the reference frame and other neighboring frames. Later, 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. Moreover, Tikhonov regularization is incorporated in the proposed framework in order to remove artifacts from the final answer and to improve the rate of convergence. Finally, experimental results are presented to demonstrate the outstanding performance of the proposed algorithm in comparison to several previously published methods using standard sequences such as Foreman and Susie that are corrupted by several noise models such as AWGN, Poisson Noise, Salt & Pepper Noise and Speckle Noise.