# SAR2EO: A High-resolution Image Translation Framework with Denoising Enhancement

arXiv: https://arxiv.org/abs/2304.04760 · HTML: https://mlsanity.com/paper/2304.04760/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 25 references that are in the map.
- First posted on arXiv 8 Apr 2023.

## Builds on (most cited first, 17 of 17)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Improved Techniques for Training GANs](https://mlsanity.com/paper/1606.03498/) (2016)
- [Conditional Generative Adversarial Nets](https://mlsanity.com/paper/1411.1784/) (2014)
- [Semantic Image Synthesis with Spatially-Adaptive Normalization](https://mlsanity.com/paper/1903.07291/) (2019)
- [High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs](https://mlsanity.com/paper/1711.11585/) (2017)
- [CyCADA: Cycle-Consistent Adversarial Domain Adaptation](https://mlsanity.com/paper/1711.03213/) (2017)
- [Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks](https://mlsanity.com/paper/1703.10593/) (2017)
- [DualGAN: Unsupervised Dual Learning for Image-to-Image Translation](https://mlsanity.com/paper/1704.02510/) (2017)
- [Toward Multimodal Image-to-Image Translation](https://mlsanity.com/paper/1711.11586/) (2017)
- [Learning to Discover Cross-Domain Relations with Generative Adversarial Networks](https://mlsanity.com/paper/1703.05192/) (2017)
- [One-Sided Unsupervised Domain Mapping](https://mlsanity.com/paper/1706.00826/) (2017)
- [ComboGAN: Unrestrained Scalability for Image Domain Translation](https://mlsanity.com/paper/1712.06909/) (2017)
- [Dual Generator Generative Adversarial Networks for Multi-Domain Image-to-Image Translation](https://mlsanity.com/paper/1901.04604/) (2019)
- [Mix and match networks: encoder-decoder alignment for zero-pair image translation](https://mlsanity.com/paper/1804.02199/) (2018)

## Built on by (most cited first, 3 of 3)

- [Improved Flood Insights: Diffusion-Based SAR to EO Image Translation](https://mlsanity.com/paper/2307.07123/) (2023)
- [Electrooptical Image Synthesis from SAR Imagery Using Generative Adversarial Networks](https://mlsanity.com/paper/2409.15331/) (2024)
- [GeoSET: Generalist Foundation Model for SAR-to-EO Image Translation](https://mlsanity.com/paper/2609.37496/) (2026)

## Abstract (arXiv)

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.