Gradient-Prior-Guided Dual-Branch Network for Preserving Fine Structures in Remote Sensing Image Super-Resolution (opens in new tab)

In the task of remote sensing image super-resolution (SR) reconstruction, both convolutional neural network (CNN) and Transformer have demonstrated impressive capabilities. However, many existing dual-branch models only perform feature fusion through simple operations such as concatenation or weighting, which fail to fully exploit the complementary advantages of CNN and Transformer in feature representation. To address this limitation, this article proposes a novel gradient-prior-guided dual-...