T<inline-formula><tex-math notation="LaTeX">$^{3}$</tex-math></inline-formula>SR: Texture Transfer Transformer for Remote Sensing Image Superresolution

Abstract

Remote sensing image superresolution has made significant progress in recent years, aiming to restore natural and realistic high-resolution images from low-resolution images. However, most image superresolution remote sensing methods are improved only by deepening their network and expanding the network size, consuming substantial computing resources and imposing a bottleneck in development. Here, we propose an end-to-end image superresolution network called texture transfer transformer for remote sensing image superresolution (T$^{3}$SR). For the first time, T$^{3}$SR introduces image texture transfer into remote sensing, which achieves the most advanced results. Specifically, T$^{3}$SR divides image superresolution into two stages: texture transfer and feature fusion. First, to solve the problems of missing textures, artifacts, and blurring in a single image superresolution approach, we design a texture transfer module to serve the shallow texture transfer. Second, to further reduce the dependence of the model on the reference image, we propose a U-Transformer-based feature fusion scheme to reduce the dependence on the reference image. Finally, we conduct numerous experiments on standard public datasets to fully evaluate our approach. In addition to verifying the method's superiority based on the reference image paradigm, we also test the performance without the reference image. All results show that our method yields an abundant texture and finish with better visual results. Moreover, the best score is also obtained in the quantitative parameters of PSNR and SSIM. Compared with the best available approach, T$^{3}$SR has an improved performance by 0.79 dB and 0.33 dB in the datasets of WHU-RS19 and RSSCN7, respectively.

Keywords

• Image superresolution
• reference image
• self-reference
• texture transfer