# SAR to Optical Image Translation with Color Supervised Diffusion Model

arXiv: https://arxiv.org/abs/2407.16921 · HTML: https://mlsanity.com/paper/2407.16921/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in Inverse problems and image restoration, one of 14 directions in Diffusion models.
- Builds on 7 of its 10 references that are in the map.
- First posted on arXiv 24 Jul 2024.

## Placed in

- Diffusion models / [Inverse problems and image restoration](https://mlsanity.com/diffusion/inverse-problems-and-image-restoration/)

## Builds on (most cited first, 7 of 7)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [DSLR-Quality Photos on Mobile Devices with Deep Convolutional Networks](https://mlsanity.com/paper/1704.02470/) (2017)
- [Reusing Discriminators for Encoding: Towards Unsupervised Image-to-Image Translation](https://mlsanity.com/paper/2003.00273/) (2020)
- [The SEN1-2 Dataset for Deep Learning in SAR-Optical Data Fusion](https://mlsanity.com/paper/1807.01569/) (2018)
- [Reciprocal Translation between SAR and Optical Remote Sensing Images with Cascaded-Residual Adversarial Networks](https://mlsanity.com/paper/1901.08236/) (2019)

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

- [Conditional Brownian Bridge Diffusion Model for VHR SAR to Optical Image Translation](https://mlsanity.com/paper/2408.07947/) (2024)
- [C-DiffSET: Leveraging Latent Diffusion for SAR-to-EO Image Translation with Confidence-Guided Reliable Object Generation](https://mlsanity.com/paper/2411.10788/) (2024)
- [Unpaired Object-Level SAR-to-Optical Image Translation for Aircraft with Keypoints-Guided Diffusion Models](https://mlsanity.com/paper/2503.19798/) (2025)

## Abstract (arXiv)

Synthetic Aperture Radar (SAR) offers all-weather, high-resolution imaging capabilities, but its complex imaging mechanism often poses challenges for interpretation. In response to these limitations, this paper introduces an innovative generative model designed to transform SAR images into more intelligible optical images, thereby enhancing the interpretability of SAR images. Specifically, our model backbone is based on the recent diffusion models, which have powerful generative capabilities. We employ SAR images as conditional guides in the sampling process and integrate color supervision to counteract color shift issues effectively. We conducted experiments on the SEN12 dataset and employed quantitative evaluations using peak signal-to-noise ratio, structural similarity, and fr'echet inception distance. The results demonstrate that our model not only surpasses previous methods in quantitative assessments but also significantly enhances the visual quality of the generated images.