Recovering Cloud Microstructures with Cascaded Diffusion Inversion

2026-02-06 · Source: cs.CV updates on arXiv.org · Field: Science & Research — Environmental Science & Earth Systems, Mathematics & Computational Sciences · Depth: Expert, long

Summary

A two-stage diffusion-based super-resolution framework has been developed to enhance multi-spectral cloud microstructure imagery by a factor of 4x. This framework addresses the limitations of existing super-resolution methods that struggle with satellite-captured spectral images due to cross-sensor differences and fine texture recovery. Stage 1 of the proposed method utilizes real-world paired data, such as SEVIRI→VIIRS and MSG→MTG, to learn robust degradation handling and inter-sensor alignment. Subsequently, Stage 2 employs a self-supervised internal downgrading of high-resolution data to refine structural learning and synthesize textures. The approach significantly outperforms transformer and diffusion-based baselines in reconstruction accuracy, visual quality, and gradient preservation. Quantitatively, it achieved a PSNR of 21.25 dB and a perceptual distance of 0.28 for SEVIRI→VIIRS, and a PSNR of 24.0 dB and a perceptual distance of 0.29 for MSG→MTG, with gradient ratios close to the ideal 1.0. The code and models are publicly available.

Key takeaway

For AI Scientists developing super-resolution models for satellite imagery, you should consider a cascaded diffusion inversion approach. This method effectively addresses challenges like cross-sensor misalignment and fine texture recovery, which traditional SR models often struggle with. By decoupling alignment and detail recovery into two stages, your models can achieve superior fidelity and structural preservation. Implement the publicly available code to enhance cloud microphysics analysis, especially for applications like cloud seeding decisions, ensuring more accurate and consistent high-resolution outputs.

Key insights

A two-stage diffusion inversion framework effectively super-resolves multi-spectral cloud imagery by decoupling cross-sensor alignment from fine-detail recovery.

Principles

• Decouple cross-sensor robustness from texture recovery.
• Real paired data learns radiometric consistency.
• Synthetic degradations refine fine-scale structure.

Method

A cascaded diffusion inversion SR framework uses Stage 1 for cross-sensor alignment with real paired data, then Stage 2 for structural refinement using self-supervised synthetic degradations on HR-only imagery.

In practice

• Apply 1-step reverse chain for efficient base estimation.
• Use PSNR, gradient preservation, perceptual distance for SR evaluation.
• Publicly available code for cloud microphysics SR.

Topics

• Diffusion Models
• Super-Resolution
• Cloud Microphysics
• Satellite Imagery
• Weather Modification
• Multi-spectral Imaging

Code references

• hananshafi/superresolution-cloud-microphysics

Best for: Computer Vision Engineer, AI Scientist, Research Scientist, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CV updates on arXiv.org.