Recently, reference-based image super-resolution (RefSR) has shown excellent
performance in image super-resolution (SR) tasks. The main idea of RefSR is to
utilize additional information from the reference (Ref) image to recover the
high-frequency components in low-resolution (LR) images. By transferring
relevant textures through feature matching, RefSR models outperform existing
single image super-resolution (SISR) models. However, their performance
significantly declines when a domain gap between Ref and LR images exists,
which often occurs in real-world scenarios, such as satellite imaging. In this
letter, we introduce a Domain Matching (DM) module that can be seamlessly
integrated with existing RefSR models to enhance their performance in a
plug-and-play manner. To the best of our knowledge, we are the first to explore
Domain Matching-based RefSR in remote sensing image processing. Our analysis
reveals that their domain gaps often occur in different satellites, and our
model effectively addresses these challenges, whereas existing models struggle.
Our experiments demonstrate that the proposed DM module improves SR performance
both qualitatively and quantitatively for remote sensing super-resolution
tasks.

利用领域匹配（DM）模块改进了参考图像超分辨率（RefSR）模型，在遥感图像超分辨率任务中取得了定性和定量上的性能提升。

跨领域对齐：一种用于遥感图像基于参考的超分辨率的简单领域匹配方法

Bridging the Domain Gap: A Simple Domain Matching Method for  Reference-based Image Super-Resolution in Remote Sensing

Recent methods for single image super-resolution (SISR) have demonstrated
outstanding performance in generating high-resolution (HR) images from
low-resolution (LR) images. However, most of these methods show their
superiority using synthetically generated LR images, and their generalizability
to real-world images is often not satisfactory. In this paper, we pay attention
to two well-known strategies developed for robust super-resolution (SR), i.e.,
reference-based SR (RefSR) and zero-shot SR (ZSSR), and propose an integrated
solution, called reference-based zero-shot SR (RZSR). Following the principle
of ZSSR, we train an image-specific SR network at test time using training
samples extracted only from the input image itself. To advance ZSSR, we obtain
reference image patches with rich textures and high-frequency details which are
also extracted only from the input image using cross-scale matching. To this
end, we construct an internal reference dataset and retrieve reference image
patches from the dataset using depth information. Using LR patches and their
corresponding HR reference patches, we train a RefSR network that is embodied
with a non-local attention module. Experimental results demonstrate the
superiority of the proposed RZSR compared to the previous ZSSR methods and
robustness to unseen images compared to other fully supervised SISR methods.

该研究提出了一种被称为参考零样例超分辨 (RZSR) 的方法，该方法结合了参考超分辨 (RefSR) 和零样例超分辨 (ZSSR) 两种策略，利用内部参考数据集和非局部注意力模块，从图像自身提取纹理丰富、高频细节的参考图像块，训练出一种图像特定的超分辨网络，在超分辨任务中具有出色表现。