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Related Experiment Videos

Global-local uncertainty-contrastive physics-guided inversion network for unsupervised cloud removal.

Shiwen Gong¹, Guanbo Feng², Qiong Liu¹

¹School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan Hubei, 430074, China.

Neural Networks : the Official Journal of the International Neural Network Society

|May 17, 2026

Summary

This summary is machine-generated.

This study introduces GUPI-Net, an advanced unsupervised cloud removal method. It enhances image quality by incorporating physical principles and adaptive loss weighting, outperforming existing techniques.

Keywords:

Cloud removal Global-local Physics-guided Remote sensing image Uncertainty-contrastive

Related Experiment Videos

Area of Science:

Remote Sensing
Computer Vision
Image Processing

Background:

Unsupervised cloud removal methods have advanced but struggle with image constraints, leading to quality limitations.
Existing models often neglect cloud formation physics, causing color distortions and structural issues.

Purpose of the Study:

To develop an unsupervised cloud removal network that addresses limitations of current methods.
To improve reconstructed image quality by incorporating physical constraints and adaptive learning.

Main Methods:

Proposed GUPI-Net featuring a Bidirectional Global-Local Uncertainty-Contrastive framework (BGLUC) for adaptive loss weighting.
Introduced a Physics-Guided Inversion Module (PGIM) to reformulate the atmospheric scattering model in the feature domain.
Constructed the CloudQuad dataset for benchmarking multi-surface remote sensing image cloud removal.

Main Results:

GUPI-Net demonstrated superior performance compared to state-of-the-art unsupervised haze and cloud removal methods.
The BGLUC framework effectively restored details in high-uncertainty regions while maintaining global consistency.
The PGIM module ensured physically consistent feature representations aligned with atmospheric scattering.

Conclusions:

GUPI-Net offers a significant advancement in unsupervised cloud removal for remote sensing imagery.
The integration of physics-guided principles and uncertainty-based learning enhances restoration quality.
The proposed methods and dataset provide valuable resources for future research in this domain.