Cloud Removal

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

2 leaderboard tables shown for this task, 2 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted.

Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

3 datasets whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

No subtask under this task in the archive's task tree.

Parent tasks archive 2025-07-28

Most implemented papers archive 2025-07-28

29 shown of 29 papers with code (56 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• Cloud removal in remote sensing images using generative adversarial networks and SAR-to-optical image translation
  22 Dec 2020 3 repositories listed
  We used the SEN1-2 dataset to train and test both GANs, and we made cloudy images by adding synthetic clouds to optical images.
• Cloud Removal in Satellite Images Using Spatiotemporal Generative Networks
  14 Dec 2019 3 repositories listed
  In contrast, we cast the problem of cloud removal as a conditional image synthesis challenge, and we propose a trainable spatiotemporal generator network (STGAN) to remove clouds.
• PMAA: A Progressive Multi-scale Attention Autoencoder Model for High-performance Cloud Removal from Multi-temporal Satellite Imagery
  29 Mar 2023 2 repositories listed Syntology ran 0 of 1 samples · 1 unverified
  Satellite imagery analysis plays a pivotal role in remote sensing; however, information loss due to cloud cover significantly impedes its application.
• Cloud Removal for Remote Sensing Imagery via Spatial Attention Generative Adversarial Network
  28 Sep 2020 2 repositories listed
  Optical remote sensing imagery has been widely used in many fields due to its high resolution and stable geometric properties.
• A Remote Sensing Image Dataset for Cloud Removal
  3 Jan 2019 2 repositories listed
  Removing clouds is an indispensable pre-processing step in remote sensing image analysis.
• Image Restoration via Multi-domain Learning
  7 May 2025 1 repository listed
  Due to adverse atmospheric and imaging conditions, natural images suffer from various degradation phenomena.
• DGMR: Diffusion Guided Masked Reconstruction Framework for Multimodal Cloud Removal
  1 May 2025 1 repository listed
  Multimodal techniques rely on synthetic aperture radar (SAR) images to recover cloudy pixels in optical images.
• Effective Cloud Removal for Remote Sensing Images by an Improved Mean-Reverting Denoising Model with Elucidated Design Space
  31 Mar 2025 1 repository listed
  To overcome this drawback, we develop a new CR model EMRDM based on mean-reverting diffusion models (MRDMs) to establish a direct diffusion process between cloudy and cloudless images.
• Attentive Contextual Attention for Cloud Removal
  20 Nov 2024 1 repository listed
  By integrating the AC-Attention module into the DSen2-CR cloud removal framework, we significantly improve the model's ability to capture essential distant information, leading to more effective cloud removal.
• AllClear: A Comprehensive Dataset and Benchmark for Cloud Removal in Satellite Imagery
  31 Oct 2024 1 repository listed Syntology ran 1 of 1 samples · 0 unverified
  Clouds in satellite imagery pose a significant challenge for downstream applications.
• MRF-Net: An Infrared Remote Sensing Image Thin Cloud Removal Method With the Intra-Inter Coherent Constraint
  7 Oct 2024 1 repository listed
  To address this problem, we proposed the multiscale residual fusion network (MRF-Net) to remove thin cloud from infrared remote sensing imagery.
• Multi-scale Restoration of Missing Data in Optical Time-series Images with Masked Spatial-Temporal Attention Network
  19 Jun 2024 1 repository listed
  Remote sensing images often suffer from substantial data loss due to factors such as thick cloud cover and sensor limitations.
• Bridging Remote Sensors with Multisensor Geospatial Foundation Models
  1 Apr 2024 1 repository listed
  A key discovery of our research is that representations derived from natural images are not always compatible with the distinct characteristics of geospatial remote sensors, underscoring the limitations of existing…
• IDF-CR: Iterative Diffusion Process for Divide-and-Conquer Cloud Removal in Remote-sensing Images
  18 Mar 2024 1 repository listed
  IDF-CR consists of a pixel space cloud removal module (Pixel-CR) and a latent space iterative noise diffusion network (IND).
• Diffusion Enhancement for Cloud Removal in Ultra-Resolution Remote Sensing Imagery
  25 Jan 2024 1 repository listed
  The presence of cloud layers severely compromises the quality and effectiveness of optical remote sensing (RS) images.
• DiffCR: A Fast Conditional Diffusion Framework for Cloud Removal from Optical Satellite Images
  8 Aug 2023 1 repository listed Syntology ran 17 of 20 samples · 3 unverified · 20 pointer-only (licence)
  Optical satellite images are a critical data source; however, cloud cover often compromises their quality, hindering image applications and analysis.
• U-TILISE: A Sequence-to-sequence Model for Cloud Removal in Optical Satellite Time Series
  22 May 2023 1 repository listed Syntology ran 0 of 14 samples · 14 unverified
  Satellite image time series in the optical and infrared spectrum suffer from frequent data gaps due to cloud cover, cloud shadows, and temporary sensor outages.
• UnCRtainTS: Uncertainty Quantification for Cloud Removal in Optical Satellite Time Series
  11 Apr 2023 1 repository listed
  Clouds and haze often occlude optical satellite images, hindering continuous, dense monitoring of the Earth's surface.
• Multi-Modal and Multi-Resolution Data Fusion for High-Resolution Cloud Removal: A Novel Baseline and Benchmark
  9 Jan 2023 1 repository listed
  With this dataset, we consider the problem of cloud removal in high-resolution optical remote sensing imagery by integrating multi-modal and multi-resolution information.
• GLF-CR: SAR-Enhanced Cloud Removal with Global-Local Fusion
  6 Jun 2022 1 repository listed
  The challenge of the cloud removal task can be alleviated with the aid of Synthetic Aperture Radar (SAR) images that can penetrate cloud cover.
• SEN12MS-CR-TS: A Remote Sensing Data Set for Multi-modal Multi-temporal Cloud Removal
  24 Jan 2022 1 repository listed
  About half of all optical observations collected via spaceborne satellites are affected by haze or clouds.
• SSSNET: Semi-Supervised Signed Network Clustering
  13 Oct 2021 1 repository listed Syntology ran 1 of 13 samples · 12 unverified
  Node embeddings are a powerful tool in the analysis of networks; yet, their full potential for the important task of node clustering has not been fully exploited.
• Panoptic Segmentation of Satellite Image Time Series with Convolutional Temporal Attention Networks
  16 Jul 2021 1 repository listed Syntology ran 10 of 11 samples · 1 unverified
  We also introduce PASTIS, the first open-access SITS dataset with panoptic annotations.
• Spatio-Temporal SAR-Optical Data Fusion for Cloud Removal via a Deep Hierarchical Model
  23 Jun 2021 1 repository listed
  Cloud removal is a relevant topic in Remote Sensing as it fosters the usability of high-resolution optical images for Earth monitoring and study.
• Seeing Through Clouds in Satellite Images
  15 Jun 2021 1 repository listed Syntology ran 0 of 3 samples · 3 unverified
  This paper presents a neural-network-based solution to recover pixels occluded by clouds in satellite images.
• Multi-Sensor Data Fusion for Cloud Removal in Global and All-Season Sentinel-2 Imagery
  16 Sep 2020 1 repository listed
  This work has been accepted by IEEE TGRS for publication.
• Cloud removal in Sentinel-2 imagery using a deep residual neural network and SAR-optical data fusion
  2 Jul 2020 1 repository listed
  Optical remote sensing imagery is at the core of many Earth observation activities.
• SAR TO OPTICAL IMAGE SYNTHESIS FOR CLOUD REMOVAL WITH GENERATIVE ADVERSARIAL NETWORKS
  26 Sep 2018 1 repository listed
  In this work, we combine the fact that SAR images are hardly affected by clouds with the ability of cGANS for image translation in order to map optical images from SAR ones so as to recover regions that are covered by…
• Missing Data Reconstruction in Remote Sensing image with a Unified Spatial-Temporal-Spectral Deep Convolutional Neural Network
  23 Feb 2018 1 repository listed
  Because of the internal malfunction of satellite sensors and poor atmospheric conditions such as thick cloud, the acquired remote sensing data often suffer from missing information, i.

Syntology lines on 7 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.