This
letter proposes a reconstruction-based single image super resolution (SR)
method by using joint regularization, where a group-residual-based
regularization (GRR) and a ridge-regression-based regularization (3R) are combined. In GRR, non-local similar patches are grouped
together, and the group weights are calculated so as to
adaptively constrain the residual values in the gradient domain. In 3R, we
adopt the ridge-regression-based method to establish the projection matrices
from an external high-resolution (HR) training set, so that the external HR
information can be utilized. To obtain an estimation
of the targeted HR image, an efficient algorithm is designed
for solving the joint formulation. Experimental results on different image datasets
indicate that the proposed method is able to achieve the state-of-the-art
performance.
Index Terms—Super Resolution,
Non-local Self-similarity, Total Variation, Ridge Regression, Regularization.
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.