SIEN:Spatial Information Enhancement Network for Blind Super-Resolution

Blind super-resolution methods based on deep learning have attracted increasing interest due to their excellent performance. However, most existing blind SR models underutilize the contextual information of features in LR images, thus relatively reducing the texture details of the images. In this paper, we propose a spatial information enhancement network (SIEN) to address this problem in image blind SR. The network adds a new branch SET module to the idea of alternate iterative optimization of image and blur kernels in blind SR, which acquires global contextual information by making full use of LR image features and restores texture details based on the contextual information. And after extensive experiments, it is proved that our proposed method can have more accurate SR results under the real world.