Synthetic Aperture Radar (SAR) to electro-optical (EO) image translation is a
fundamental task in remote sensing that can enrich the dataset by fusing
information from different sources. Recently, many methods have been proposed
to tackle this task, but they are still difficult to complete the conversion
from low-resolution images to high-resolution images. Thus, we propose a
framework, SAR2EO, aiming at addressing this challenge. Firstly, to generate
high-quality EO images, we adopt the coarse-to-fine generator, multi-scale
discriminators, and improved adversarial loss in the pix2pixHD model to
increase the synthesis quality. Secondly, we introduce a denoising module to
remove the noise in SAR images, which helps to suppress the noise while
preserving the structural information of the images. To validate the
effectiveness of the proposed framework, we conduct experiments on the dataset
of the Multi-modal Aerial View Imagery Challenge (MAVIC), which consists of
large-scale SAR and EO image pairs. The experimental results demonstrate the
superiority of our proposed framework, and we win the first place in the MAVIC
held in CVPR PBVS 2023.

该篇论文提出了 SAR2EO 框架，使用粗到细的生成器、多尺度判别器和改进的对抗性损失生成高质量的 EO 图像，并引入降噪模块来去除 SAR 图像的噪声。证实了该框架在 MAVIC 数据集上效果显著，赢得了 CVPR PBVS 2023 的第一名。