Video Enhancement Based on A Robust Hampel Iterative SRR with a General Observation Model

This paper proposes a novel robust SuperResolution Reconstruction (SRR) framework that can enhance a real complex video sequence and is applicable to any noise models. Although SRR algorithms have received considerable attention within the traditional research community, these algorithms are typically very sensitive to their assumed model of data and noise, which limits their utility. The real noise models that corrupt the measured sequence are unknown; consequently, SRR algorithms using L1 or L2 norm may degrade the image sequence rather than enhance it therefore the robust norm applicable to several noise and data models is desired in SRR algorithms. This paper proposes a SRR framework based on the stochastic regularization technique of Bayesian MAP estimation by minimizing a cost function. In order to tolerate to any noise models, the Hampel norm is used for measuring the difference between the projected estimate of the high-resolution image and each low resolution image, and removing outliers in the data. Tikhonov regularization is used to remove artifacts from the final result and improve the rate of convergence. Moreover, in order to cope with real video sequences and complex motion sequences, this paper proposes a SRR General Observation Model (GOM or affine block-based transform) devoted to the case of nonisometric inter-frame motion. In the experimental section, the proposed framework can enhance real complex motion sequences, such as Suzie and Foreman sequence, and confirm the effectiveness of our algorithm and demonstrate its superiority to other SRR algorithms based on L1 and L2 norm for several noise models (such as AWGN, Poisson noise, Salt & Pepper noise and Speckle noise) at several noise power.