  Video super-resolution reconstruction (SRR) algorithms attempt to reconstruct
high-resolution (HR) video sequences from low-resolution observations. Although
recent progress in video SRR has significantly improved the quality of the
reconstructed HR sequences, it remains challenging to design SRR algorithms
that achieve good quality and robustness at a small computational complexity,
being thus suitable for online applications. In this paper, we propose a new
adaptive video SRR algorithm that achieves state-of-the-art performance at a
very small computational cost. Using a nonlinear cost function constructed
considering characteristics of typical innovation outliers in natural image
sequences and an edge-preserving regularization strategy, we achieve
state-of-the-art reconstructed image quality and robustness. This cost function
is optimized using a specific alternating projections strategy over non-convex
sets that is able to converge in a very few iterations. An accurate and very
efficient approximation for the projection operations is also obtained using
tools from multidimensional multirate signal processing. This solves the slow
convergence issue of stochastic gradient-based methods while keeping a small
computational complexity. Simulation results with both synthetic and real image
sequences show that the performance of the proposed algorithm is similar or
better than state-of-the-art SRR algorithms, while requiring only a small
fraction of their computational cost.
