# High-Quality Cloud-Free Optical Image Synthesis Using Multi-Temporal SAR and Contaminated Optical Data

arXiv: https://arxiv.org/abs/2504.16870 · HTML: https://mlsanity.com/paper/2504.16870/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 42 references that are in the map.
- First posted on arXiv 23 Apr 2025.

## Builds on (most cited first, 11 of 11)

- [Swin Transformer V2: Scaling Up Capacity and Resolution](https://mlsanity.com/paper/2111.09883/) (2021)
- [Least Squares Generative Adversarial Networks](https://mlsanity.com/paper/1611.04076/) (2016)
- [Multimodal Unsupervised Image-to-Image Translation](https://mlsanity.com/paper/1804.04732/) (2018)
- [Toward Multimodal Image-to-Image Translation](https://mlsanity.com/paper/1711.11586/) (2017)
- [ResViT: Residual vision transformers for multi-modal medical image synthesis](https://mlsanity.com/paper/2106.16031/) (2021)
- [UNetFormer: A UNet-like Transformer for Efficient Semantic Segmentation of Remote Sensing Urban Scene Imagery](https://mlsanity.com/paper/2109.08937/) (2021)
- [Reusing Discriminators for Encoding: Towards Unsupervised Image-to-Image Translation](https://mlsanity.com/paper/2003.00273/) (2020)
- [Missing Data Reconstruction in Remote Sensing image with a Unified Spatial-Temporal-Spectral Deep Convolutional Neural Network](https://mlsanity.com/paper/1802.08369/) (2018)
- [GLF-CR: SAR-Enhanced Cloud Removal with Global-Local Fusion](https://mlsanity.com/paper/2206.02850/) (2022)
- [Multi-Modal and Multi-Resolution Data Fusion for High-Resolution Cloud Removal: A Novel Baseline and Benchmark](https://mlsanity.com/paper/2301.03432/) (2023)
- [Thick Cloud Removal of Remote Sensing Images Using Temporal Smoothness and Sparsity-Regularized Tensor Optimization](https://mlsanity.com/paper/2008.04529/) (2020)

## Abstract (arXiv)

Addressing gaps caused by cloud cover and the long revisit cycle of satellites is vital for providing essential data to support remote sensing applications. This paper tackles the challenges of missing optical data synthesis, particularly in complex scenarios with cloud cover. We propose CRSynthNet, a novel image synthesis network that incorporates innovative designed modules such as the DownUp Block and Fusion Attention to enhance accuracy. Experimental results validate the effectiveness of CRSynthNet, demonstrating substantial improvements in restoring structural details, preserving spectral consist, and achieving superior visual effects that far exceed those produced by comparison methods. It achieves quantitative improvements across multiple metrics: a peak signal-to-noise ratio (PSNR) of 26.978, a structural similarity index measure (SSIM) of 0.648, and a root mean square error (RMSE) of 0.050. Furthermore, this study creates the TCSEN12 dataset, a valuable resource specifically designed to address cloud cover challenges in missing optical data synthesis study. The dataset uniquely includes cloud-covered images and leverages earlier image to predict later image, offering a realistic representation of real-world scenarios. This study offer practical method and valuable resources for optical satellite image synthesis task.