# GLF-CR: SAR-Enhanced Cloud Removal with Global-Local Fusion

arXiv: https://arxiv.org/abs/2206.02850 · HTML: https://mlsanity.com/paper/2206.02850/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 53 references that are in the map.
- First posted on arXiv 6 Jun 2022.

## Builds on (most cited first, 24 of 24)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [SwinIR: Image Restoration Using Swin Transformer](https://mlsanity.com/paper/2108.10257/) (2021)
- [Enhanced Deep Residual Networks for Single Image Super-Resolution](https://mlsanity.com/paper/1707.02921/) (2017)
- [Image Super-Resolution Using Very Deep Residual Channel Attention Networks](https://mlsanity.com/paper/1807.02758/) (2018)
- [Residual Dense Network for Image Super-Resolution](https://mlsanity.com/paper/1802.08797/) (2018)
- [DOTA: A Large-scale Dataset for Object Detection in Aerial Images](https://mlsanity.com/paper/1711.10398/) (2017)
- [Dynamic Filter Networks](https://mlsanity.com/paper/1605.09673/) (2016)
- [Spatial Attentive Single-Image Deraining with a High Quality Real Rain Dataset](https://mlsanity.com/paper/1904.01538/) (2019)
- [Residual Dense Network for Image Restoration](https://mlsanity.com/paper/1812.10477/) (2018)
- [Pluralistic Image Completion](https://mlsanity.com/paper/1903.04227/) (2019)
- [Spatio-Temporal Filter Adaptive Network for Video Deblurring](https://mlsanity.com/paper/1904.12257/) (2019)
- [JL-DCF: Joint Learning and Densely-Cooperative Fusion Framework for RGB-D Salient Object Detection](https://mlsanity.com/paper/2004.08515/) (2020)
- [Learning A Single Network for Scale-Arbitrary Super-Resolution](https://mlsanity.com/paper/2004.03791/) (2020)
- [Beyond RGB: Very High Resolution Urban Remote Sensing With Multimodal Deep Networks](https://mlsanity.com/paper/1711.08681/) (2017)
- [Deep Learning Meets SAR](https://mlsanity.com/paper/2006.10027/) (2020)
- [Cross-Modal Collaborative Representation Learning and a Large-Scale RGBT Benchmark for Crowd Counting](https://mlsanity.com/paper/2012.04529/) (2020)
- [Motion Deblurring with Real Events](https://mlsanity.com/paper/2109.13695/) (2021)
- [Multi-Sensor Data Fusion for Cloud Removal in Global and All-Season Sentinel-2 Imagery](https://mlsanity.com/paper/2009.07683/) (2020)
- [EarthNet2021: A large-scale dataset and challenge for Earth surface forecasting as a guided video prediction task](https://mlsanity.com/paper/2104.10066/) (2021)
- [SEN12MS-CR-TS: A Remote Sensing Data Set for Multi-modal Multi-temporal Cloud Removal](https://mlsanity.com/paper/2201.09613/) (2022)
- [Cloud Removal for Remote Sensing Imagery via Spatial Attention Generative Adversarial Network](https://mlsanity.com/paper/2009.13015/) (2020)
- [Filmy Cloud Removal on Satellite Imagery with Multispectral Conditional Generative Adversarial Nets](https://mlsanity.com/paper/1710.04835/) (2017)
- [Leveraging Crowdsourced GPS Data for Road Extraction from Aerial Imagery](https://mlsanity.com/paper/1905.01447/) (2019)

## Built on by (most cited first, 24 of 24)

- [Bridging Remote Sensors with Multisensor Geospatial Foundation Models](https://mlsanity.com/paper/2404.01260/) (2024)
- [UnCRtainTS: Uncertainty Quantification for Cloud Removal in Optical Satellite Time Series](https://mlsanity.com/paper/2304.05464/) (2023)
- [DiffCR: A Fast Conditional Diffusion Framework for Cloud Removal from Optical Satellite Images](https://mlsanity.com/paper/2308.04417/) (2023)
- [IDF-CR: Iterative Diffusion Process for Divide-and-Conquer Cloud Removal in Remote-sensing Images](https://mlsanity.com/paper/2403.11870/) (2024)
- [Effective Cloud Removal for Remote Sensing Images by an Improved Mean-Reverting Denoising Model with Elucidated Design Space](https://mlsanity.com/paper/2503.23717/) (2025)
- [U-TILISE: A Sequence-to-sequence Model for Cloud Removal in Optical Satellite Time Series](https://mlsanity.com/paper/2305.13277/) (2023)
- [Multi-Modal and Multi-Resolution Data Fusion for High-Resolution Cloud Removal: A Novel Baseline and Benchmark](https://mlsanity.com/paper/2301.03432/) (2023)
- [Attentive Contextual Attention for Cloud Removal](https://mlsanity.com/paper/2411.13042/) (2024)
- [A Progressive Image Restoration Network for High-order Degradation Imaging in Remote Sensing](https://mlsanity.com/paper/2412.07195/) (2024)
- [Task-Driven Prompt Learning: A Joint Framework for Multi-modal Cloud Removal and Segmentation](https://mlsanity.com/paper/2601.12052/) (2026)
- [SOMA-1M: A Large-Scale SAR-Optical Multi-resolution Alignment Dataset for Multi-Task Remote Sensing](https://mlsanity.com/paper/2602.05480/) (2026)
- [Multimodal Diffusion Bridge with Attention-Based SAR Fusion for Satellite Image Cloud Removal](https://mlsanity.com/paper/2504.03607/) (2025)
- [Cloud-Aware SAR Fusion for Enhanced Optical Sensing in Space Missions](https://mlsanity.com/paper/2506.17885/) (2025)
- [CloudBreaker: Breaking the Cloud Covers of Sentinel-2 Images using Multi-Stage Trained Conditional Flow Matching on Sentinel-1](https://mlsanity.com/paper/2508.03608/) (2025)
- [Remote Sensing Image Dehazing: A Systematic Review of Progress, Challenges, and Prospects](https://mlsanity.com/paper/2603.20289/) (2026)
- [ATT-CR: Adaptive Triangular Transformer for Cloud Removal](https://mlsanity.com/paper/2606.05999/) (2026)
- [MM811 Project Report: Cloud Detection and Removal in Satellite Images](https://mlsanity.com/paper/2212.11369/) (2022)
- [High-Quality Cloud-Free Optical Image Synthesis Using Multi-Temporal SAR and Contaminated Optical Data](https://mlsanity.com/paper/2504.16870/) (2025)
- [Lean Learning Beyond Clouds: Efficient Discrepancy-Conditioned Optical-SAR Fusion for Semantic Segmentation](https://mlsanity.com/paper/2603.20811/) (2026)
- [Densification and forecasting of Sentinel-2 time series from multimodal SAR and Optical satellite data using deep generative models](https://mlsanity.com/paper/2605.04239/) (2026)
- [Synthetic Aperture Radar Image Change Detection Based on Global Dynamic Context-Aware Network](https://mlsanity.com/paper/2605.16764/) (2026)
- [IB-HFN: Information Bottleneck-Driven SAR-Optical Fusion Network for High-Fidelity Cloud Removal](https://mlsanity.com/paper/2606.09347/) (2026)
- [HLS-GPT: A Generative Pretrained Transformer (GPT) for Continental-Scale NASA Harmonized Landsat and Sentinel-2 (HLS) Reflectance Reconstruction Across All Bands on Arbitrary Dates](https://mlsanity.com/paper/2606.18115/) (2026)
- [To Remove or Not to Remove Clouds: A Comparative Analysis and Fusion of Raw SAR and Synthetic NDWI for Overcast Water Segmentation](https://mlsanity.com/paper/2608.17398/) (2026)

## Abstract (arXiv)

The challenge of the cloud removal task can be alleviated with the aid of Synthetic Aperture Radar (SAR) images that can penetrate cloud cover. However, the large domain gap between optical and SAR images as well as the severe speckle noise of SAR images may cause significant interference in SAR-based cloud removal, resulting in performance degeneration. In this paper, we propose a novel global-local fusion based cloud removal (GLF-CR) algorithm to leverage the complementary information embedded in SAR images. Exploiting the power of SAR information to promote cloud removal entails two aspects. The first, global fusion, guides the relationship among all local optical windows to maintain the structure of the recovered region consistent with the remaining cloud-free regions. The second, local fusion, transfers complementary information embedded in the SAR image that corresponds to cloudy areas to generate reliable texture details of the missing regions, and uses dynamic filtering to alleviate the performance degradation caused by speckle noise. Extensive evaluation demonstrates that the proposed algorithm can yield high quality cloud-free images and outperform state-of-the-art cloud removal algorithms with a gain about 1.7dB in terms of PSNR on SEN12MS-CR dataset.