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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

Showing 110 of 56 papers

TitleStatusHype
Effective Cloud Removal for Remote Sensing Images by an Improved Mean-Reverting Denoising Model with Elucidated Design SpaceCode2
Bridging Remote Sensors with Multisensor Geospatial Foundation ModelsCode2
Diffusion Enhancement for Cloud Removal in Ultra-Resolution Remote Sensing ImageryCode2
Image Restoration via Multi-domain LearningCode1
Attentive Contextual Attention for Cloud RemovalCode1
AllClear: A Comprehensive Dataset and Benchmark for Cloud Removal in Satellite ImageryCode1
Multi-scale Restoration of Missing Data in Optical Time-series Images with Masked Spatial-Temporal Attention NetworkCode1
IDF-CR: Iterative Diffusion Process for Divide-and-Conquer Cloud Removal in Remote-sensing ImagesCode1
DiffCR: A Fast Conditional Diffusion Framework for Cloud Removal from Optical Satellite ImagesCode1
U-TILISE: A Sequence-to-sequence Model for Cloud Removal in Optical Satellite Time SeriesCode1
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