  Single image super-resolution (SISR) is an image processing task which
obtains high-resolution (HR) image from a low-resolution (LR) image. Recently,
due to the capability in feature extraction, a series of deep learning methods
have brought important crucial improvement for SISR. However, we observe that
no matter how deeper the networks are designed, they usually do not have good
generalization ability, which leads to the fact that almost all of existing SR
methods have poor performances on restoration of the weak texture details. To
solve these problems, we propose a weak texture information map guided image
super-resolution with deep residual networks. It contains three sub-networks,
one main network which extracts the main features and fuses weak texture
details, another two auxiliary networks extract the weak texture details fallen
in the main network. Two part of networks work cooperatively, the auxiliary
networks predict and integrates week texture information into the main network,
which is conducive to the main network learning more inconspicuous details.
Experiments results demonstrate that our method's performs achieve the
state-of-the-art quantitatively. Specifically, the image super-resolution
results of our method own more weak texture details.
