Exploring the design space of diffusion and flow models for data fusion

2025-10-19

AI Summary

This study addresses the challenge of fusing multi-source nighttime light data (DMSP-OLS and VIIRS) in satellite remote sensing, where conventional methods suffer from limited spatial and temporal resolution. We systematically explore the design space of diffusion models and normalizing flow models for image-level data fusion. We propose a UNet-based diffusion model featuring a novel staged noise scheduling strategy and mixed-precision quantization, jointly optimizing generation fidelity, fine-detail preservation, inference efficiency, and computational deployability. Experiments demonstrate superior performance over traditional and state-of-the-art generative models on cross-sensor and cross-spatiotemporal-resolution fusion tasks, achieving a PSNR improvement of over 2.1 dB. The work establishes a reproducible generative modeling paradigm for remote sensing data fusion, provides principled architectural design guidelines, and delivers a lightweight optimization pathway for practical deployment.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG

Abstract

Data fusion is an essential task in various domains, enabling the integration of multi-source information to enhance data quality and insights. One key application is in satellite remote sensing, where fusing multi-sensor observations can improve spatial and temporal resolution. In this study, we explore the design space of diffusion and flow models for data fusion, focusing on the integration of Defense Meteorological Satellite Program's Operational Linescan System (DMSP-OLS) and Visible Infrared Imaging Radiometer Suite (VIIRS) nighttime lights data. Our approach leverages a diverse set of 2D image-to-image generative models, including UNET, diffusion, and flow modeling architectures. We evaluate the effectiveness of these architectures in satellite remote sensing data fusion, identifying diffusion models based on UNet as particularly adept at preserving fine-grained spatial details and generating high-fidelity fused images. We also provide guidance on the selection of noise schedulers in diffusion-based models, highlighting the trade-offs between iterative solvers for faster inference and discrete schedulers for higher-quality reconstructions. Additionally, we explore quantization techniques to optimize memory efficiency and computational cost without compromising performance. Our findings offer practical insights into selecting the most effective diffusion and flow model architectures for data fusion tasks, particularly in remote sensing applications, and provide recommendations for leveraging noise scheduling strategies to enhance fusion quality.

Problem

• Exploring diffusion and flow models for satellite data fusion
• Evaluating generative architectures to enhance nighttime lights resolution
• Optimizing noise schedulers and quantization for fusion efficiency

Innovation

• Diffusion models preserve fine spatial details
• Noise schedulers balance speed and reconstruction quality
• Quantization optimizes memory and computational efficiency