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

Recent results in SRR (Super Resolution Reconstruction) demonstrate that the fusion of a sequence of low-resolution noisy blurred images can produce a higher-resolution image or sequence. Since noise is always present in practical acquisition systems, almost video enhancement algorithms are developed assuming AWGN model for the corrupting noise. When the underlying video measurements are corrupted by other noise models such as Poisson Noise, impulsive Noise (Salt&Pepper) and Speckle Noise, the enhancement algorithms fail to recover a close approximation of the video. This paper proposes an alternative robust video enhancement algorithm using SRR based on the regularization ML technique. First, the classical registration process is used to estimate the relationship between the reference frame and other neighboring frames. Subsequently, the Leclerc 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. Later, the reconstructed video frame is estimated by minimize the total cost function. Finally, experimental results are presented to demonstrate the outstanding performance of the proposed algorithm in comparison to several previously published methods.