Weak Texture Information Map Guided Image Super-resolution with Deep Residual Networks
This addresses a specific limitation in image super-resolution for applications requiring fine detail recovery, but it is incremental as it builds on existing deep residual network approaches.
The paper tackles the problem of poor generalization in deep learning-based single image super-resolution, particularly in restoring weak texture details, by proposing a method that uses auxiliary networks to guide a main network, achieving state-of-the-art quantitative results with improved weak texture detail restoration.
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.