Academic Editor: J. J. Judice
Copyright © 2011 Li-Li Huang et al. This is an open access article distributed under the
Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Super-resolution is a fusion process for reconstructing a high-resolution image from a
set of low-resolution images. This paper proposes a novel approach to image super-resolution based
on total variation (TV) regularization. We applied the Douglas-Rachford splitting technique to the
constrained TV-based variational SR model which is separated into three subproblems that are easy
to solve. Then, we derive an efficient and effective iterative scheme, which includes a fast iterative
shrinkage/thresholding algorithm for denoising problem, a very simple noniterative algorithm for
fusion part, and linear equation systems for deblurring process. Moreover, to speed up convergence,
we provide an accelerated scheme based on precondition design of initial guess and forward-backward
splitting technique which yields linear systems of equations with a nice structure. The proposed
algorithm shares a remarkable simplicity together with a proven global rate of convergence which is
significantly better than currently known lagged diffusivity fixed point iteration algorithm and fast
decoupling algorithm by exploiting the alternating minimizing approach. Experimental results are
presented to illustrate the effectiveness of the proposed algorithm.