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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 1–10 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
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