  In this paper, a new video super-resolution reconstruction (SRR) method with
improved robustness to outliers is proposed. Although the R-LMS is one of the
SRR algorithms with the best reconstruction quality for its computational cost,
and is naturally robust to registration inaccuracies, its performance is known
to degrade severely in the presence of innovation outliers. By studying the
proximal point cost function representation of the R-LMS iterative equation, a
better understanding of its performance under different situations is attained.
Using statistical properties of typical innovation outliers, a new cost
function is then proposed and two new algorithms are derived, which present
improved robustness to outliers while maintaining computational costs
comparable to that of R-LMS. Monte Carlo simulation results illustrate that the
proposed method outperforms the traditional and regularized versions of LMS,
and is competitive with state-of-the-art SRR methods at a much smaller
computational cost.
