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

It is well known the adversarial optimization of GAN-based image
super-resolution (SR) methods makes the preceding SR model generate unpleasant
and undesirable artifacts, leading to large distortion. We attribute the cause
of such distortions to the poor calibration of the discriminator, which hampers
its ability to provide meaningful feedback to the generator for learning
high-quality images. To address this problem, we propose a simple but
non-travel diffusion-style data augmentation scheme for current GAN-based SR
methods, known as DifAugGAN. It involves adapting the diffusion process in
generative diffusion models for improving the calibration of the discriminator
during training motivated by the successes of data augmentation schemes in the
field to achieve good calibration. Our DifAugGAN can be a Plug-and-Play
strategy for current GAN-based SISR methods to improve the calibration of the
discriminator and thus improve SR performance. Extensive experimental
evaluations demonstrate the superiority of DifAugGAN over state-of-the-art
GAN-based SISR methods across both synthetic and real-world datasets,
showcasing notable advancements in both qualitative and quantitative results.

GAN-based 图像超分辨率中对鉴别器进行校准的简单而非迁移式数据增强方案 DifAugGAN 能够提高鉴别器校准，从而改善超分辨率性能。在合成和真实数据集上进行的广泛实验评估表明，DifAugGAN 相对于现有的基于 GAN 的图像超分辨率方法具有显著的优越性，展示了定性和定量结果的重要进展。