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Cloud Removal

The majority of all optical observations collected via spaceborne satellites are affected by haze or clouds. Consequently, persistent cloud coverage affects the remote sensing practitioner's capabilities of a continuous and seamless monitoring of our planet. Cloud removal is the task of reconstructing cloud-covered information while preserving originally cloud-free details.

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Papers

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Showing 1–25 of 56 papers

TitleStatusHype
Image Restoration via Multi-domain LearningCode1
DGMR: Diffusion Guided Masked Reconstruction Framework for Multimodal Cloud RemovalCode0
When Cloud Removal Meets Diffusion Model in Remote Sensing—0
Cross-Frequency Implicit Neural Representation with Self-Evolving Parameters—0
SAR-to-RGB Translation with Latent Diffusion for Earth Observation—0
MIMRS: A Survey on Masked Image Modeling in Remote Sensing—0
Multimodal Diffusion Bridge with Attention-Based SAR Fusion for Satellite Image Cloud Removal—0
Effective Cloud Removal for Remote Sensing Images by an Improved Mean-Reverting Denoising Model with Elucidated Design SpaceCode2
Cloud Removal With PolSAR-Optical Data Fusion Using A Two-Flow Residual Network—0
Patch-GAN Transfer Learning with Reconstructive Models for Cloud Removal—0
Enhancing Scene Classification in Cloudy Image Scenarios: A Collaborative Transfer Method with Information Regulation Mechanism using Optical Cloud-Covered and SAR Remote Sensing ImagesCode0
Deeply Learned Robust Matrix Completion for Large-scale Low-rank Data Recovery—0
Attentive Contextual Attention for Cloud RemovalCode1
AllClear: A Comprehensive Dataset and Benchmark for Cloud Removal in Satellite ImageryCode1
MRF-Net: An Infrared Remote Sensing Image Thin Cloud Removal Method With the Intra-Inter Coherent ConstraintCode0
Removing cloud shadows from ground-based solar imagery—0
Haar Nuclear Norms with Applications to Remote Sensing Imagery Restoration—0
Multi-scale Restoration of Missing Data in Optical Time-series Images with Masked Spatial-Temporal Attention NetworkCode1
Bridging Remote Sensors with Multisensor Geospatial Foundation ModelsCode2
IDF-CR: Iterative Diffusion Process for Divide-and-Conquer Cloud Removal in Remote-sensing ImagesCode1
Diffusion Enhancement for Cloud Removal in Ultra-Resolution Remote Sensing ImageryCode2
Diffusion Models for Earth Observation Use-cases: from cloud removal to urban change detection—0
DiffCR: A Fast Conditional Diffusion Framework for Cloud Removal from Optical Satellite ImagesCode1
Cloud Removal in Remote Sensing Using Sequential-Based Diffusion Models—0
U-TILISE: A Sequence-to-sequence Model for Cloud Removal in Optical Satellite Time SeriesCode1
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