Image super-resolution (SR) is a challenging task which aims
to recover the high-resolution (HR) images from the degraded low-resolution
(LR) observations. To address this ill-posed problem, properly exploiting the
image prior is of great importance. In this paper, we propose a data-adaptive
low-rank (DLR) model. Rather than directly assuming that
the rank of a group of similar patches is low, the DLR model imposes the
low-rank property on the residual of the grouped patches. In addition, the
shape of the patches in our DLR model is adapted to the contents of images, so
that the dissimilar pixels in a group of patches can be largely reduced. In
order to further boost the performance, an external gradient prior (EGP), which
is learned externally to capture gradient information, is combined with DLR to
form a joint prior. When solving the DLR-based and the joint-prior-based
minimization problems, the split Bregman method is adopted to speed up the
convergence. The extensive experimental results show that our algorithms
outperform many state-of-the-art single image SR methods in terms of both
objective and subjective qualities.
Index Terms— Super-resolution,
Low-rank modeling, Steering kernel, Gradient prior,
Split Bregman method.
To facilitate further evaluation and exploration
of the method proposed in the above paper, we publish the source code at this link.
You are free to use the source code provided
that (1) you clearly cite the source; and (2) you do not make any
redistribution of the code.