Structure-Texture Dual Preserving for Remote Sensing Image Super Resolution

Most of the existing remote sensing image super-resolution (SR) methods based on deep learning tend to learn the mapping from low-resolution (LR) images to high-resolution (HR) images directly. But they ignore the potential structure and texture consistency of LR and HR spaces, which cause the loss of high-frequency information and produce artifacts. A structure-texture dual preserving method is proposed to solve this problem and generate pleasing details. Specifically, we propose a novel edge prior enhancement strategy that uses the edges of LR images and the proposed interactive supervised attention module (ISAM) to guide SR reconstruction. First, we introduce the LR edge map as a prior structural expression for SR reconstruction, which further enhances the SR process with edge preservation capability. In addition, to obtain finer texture edge information, we propose a novel ISAM in order to correct the initial LR edge map with high-frequency information. By introducing LR edges and ISAM-corrected HR edges, we build LR-HR edge mapping to preserve the consistency of LR and HR edge structure and texture, which provides supervised information for SR reconstruction. Finally, we explore the salient features of the image and its edges in the ascending space, and restored the difference between LR and HR images by residual and dense learning. A large number of experimental results on Draper and NWPU-RESISC45 datasets show that our model is superior to several advanced SR algorithms in both objective and subjective image quality.