  Recently, various deep-neural-network (DNN)-based approaches have been
proposed for single-image super-resolution (SISR). Despite their promising
results on major structure regions such as edges and lines, they still suffer
from limited performance on texture regions that consist of very complex and
fine patterns. This is because, during the acquisition of a low-resolution (LR)
image via down-sampling, these regions lose most of the high frequency
information necessary to represent the texture details. In this paper, we
present a novel texture enhancement framework for SISR to effectively improve
the spatial resolution in the texture regions as well as edges and lines. We
call our method, high-resolution (HR) style transfer algorithm. Our framework
consists of three steps: (i) generate an initial HR image from an interpolated
LR image via an SISR algorithm, (ii) generate an HR style image from the
initial HR image via down-scaling and tiling, and (iii) combine the HR style
image with the initial HR image via a customized style transfer algorithm.
Here, the HR style image is obtained by down-scaling the initial HR image and
then repetitively tiling it into an image of the same size as the HR image.
This down-scaling and tiling process comes from the idea that texture regions
are often composed of small regions that similar in appearance albeit sometimes
different in scale. This process creates an HR style image that is rich in
details, which can be used to restore high-frequency texture details back into
the initial HR image via the style transfer algorithm. Experimental results on
a number of texture datasets show that our proposed HR style transfer algorithm
provides more visually pleasing results compared with competitive methods.
