DTESR: Remote Sensing Imagery Super-Resolution With Dynamic Reference Textures Exploitation

Abstract

Reference-based remote sensing super-resolution (RefRS-SR) method shows great potential for improving both spatial resolution and coverage area of remote sensing images, by which high-resolution (HR) reference images can supplement fine details for low-resolution (LR) but wide coverage images. However, most RefRS-SR methods treat the reference as a static template and unidirectionally transfer the high-frequency information to the LR input. To address the issue of inefficient and inaccurate guided super-resolving, we propose a new RefRS-SR method with dynamic reference textures exploitation dubbed DTESR. The key referenced restoration (Ref Restoration) module consists of three components: correlation generation, texture enhancement and refinement (TER), and adaptive similarity-based fusion to progressively reconstruct high correlation and delicate textures for the LR input. Specifically, both the LR input and reference features are utilized for precise correlation generation. Next, both features are enhanced and refined with the most suitable reference under the guidance of the correlation map. Moreover, a learnable fusion method is designed to maintain the consistency of adjacent pixels. These operations will be iteratively applied to the three reconstruction scales to promote the exploitation of the Ref features. Through comprehensive quantitative and qualitative evaluations, our experimental results demonstrate that DTESR surpasses the current state-of-the-art RefRS-SR methods.