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

• URL: http://arxiv.org/abs/2510.21791v1
• Date: Mon, 20 Oct 2025 01:03:08 GMT
• Title: Exploring the design space of diffusion and flow models for data fusion
• Authors: Niraj Chaudhari, Manmeet Singh, Naveen Sudharsan, Amit Kumar Srivastava, Harsh Kamath, Dushyant Mahajan, Ayan Paul,
• Abstract summary: This study focuses on the integration of Defense Meteorological Satellite Program's Operational Linescan System (DMSP-OLS) and Visible Infrared Imaging Radiometer Suite (VIIRS) nighttime lights data.<n>We evaluate the effectiveness of 2D image-to-image generative models, including UNET, diffusion, and flow modeling architectures.
• Score: 0.27362348479453213
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• DIFF-MF: A Difference-Driven Channel-Spatial State Space Model for Multi-Modal Image Fusion [51.07069814578009]
  Multi-modal image fusion aims to integrate complementary information from multiple source images to produce high-quality fused images with enriched content.<n>We propose DIFF-MF, a novel difference-driven channel-spatial state space model for multi-modal image fusion.<n>Our method outperforms existing approaches in both visual quality and quantitative evaluation.
  arXiv  Detail & Related papers  (2026-01-09T05:26:54Z)
• A Novel Cloud-Based Diffusion-Guided Hybrid Model for High-Accuracy Accident Detection in Intelligent Transportation Systems [21.082870778158313]
  We present a novel hybrid model integrating guidance classification with diffusion techniques.<n>Our implementation is cloud-based, enabling scalable and efficient processing.<n>The proposed diffusion model performs best in image-based accident detection with an accuracy of 97.32%.
  arXiv  Detail & Related papers  (2025-10-04T05:02:15Z)
• Efficient Rectified Flow for Image Fusion [48.330480065862474]
  We propose RFfusion, an efficient one-step diffusion model for image fusion based on Rectified Flow.<n>We also propose a task-specific variational autoencoder architecture tailored for image fusion.<n>Our method outperforms other state-of-the-art methods in terms of both inference speed and fusion quality.
  arXiv  Detail & Related papers  (2025-09-20T06:21:00Z)
• A Diffusion-Based Framework for Terrain-Aware Remote Sensing Image Reconstruction [4.824120664293887]
  SatelliteMaker is a diffusion-based method that reconstructs missing data across varying levels of data loss.<n>Digital Elevation Model (DEM) as a conditioning input and use tailored prompts to generate realistic images.<n>VGG-Adapter module based on Distribution Loss, which reduces distribution discrepancy and ensures style consistency.
  arXiv  Detail & Related papers  (2025-04-16T14:19:57Z)
• Diffusion Models Without Attention [110.5623058129782]
  Diffusion State Space Model (DiffuSSM) is an architecture that supplants attention mechanisms with a more scalable state space model backbone. Our focus on FLOP-efficient architectures in diffusion training marks a significant step forward.
  arXiv  Detail & Related papers  (2023-11-30T05:15:35Z)
• SatDM: Synthesizing Realistic Satellite Image with Semantic Layout Conditioning using Diffusion Models [0.0]
  Denoising Diffusion Probabilistic Models (DDPMs) have demonstrated significant promise in synthesizing realistic images from semantic layouts. In this paper, a conditional DDPM model capable of taking a semantic map and generating high-quality, diverse, and correspondingly accurate satellite images is implemented. The effectiveness of our proposed model is validated using a meticulously labeled dataset introduced within the context of this study.
  arXiv  Detail & Related papers  (2023-09-28T19:39:13Z)
• EPMF: Efficient Perception-aware Multi-sensor Fusion for 3D Semantic Segmentation [62.210091681352914]
  We study multi-sensor fusion for 3D semantic segmentation for many applications, such as autonomous driving and robotics. In this work, we investigate a collaborative fusion scheme called perception-aware multi-sensor fusion (PMF) We propose a two-stream network to extract features from the two modalities separately. The extracted features are fused by effective residual-based fusion modules.
  arXiv  Detail & Related papers  (2021-06-21T10:47:26Z)
• Multi-Scale Boosted Dehazing Network with Dense Feature Fusion [92.92572594942071]
  We propose a Multi-Scale Boosted Dehazing Network with Dense Feature Fusion based on the U-Net architecture. We show that the proposed model performs favorably against the state-of-the-art approaches on the benchmark datasets as well as real-world hazy images.
  arXiv  Detail & Related papers  (2020-04-28T09:34:47Z)
• Spatial-Spectral Residual Network for Hyperspectral Image Super-Resolution [82.1739023587565]
  We propose a novel spectral-spatial residual network for hyperspectral image super-resolution (SSRNet) Our method can effectively explore spatial-spectral information by using 3D convolution instead of 2D convolution, which enables the network to better extract potential information. In each unit, we employ spatial and temporal separable 3D convolution to extract spatial and spectral information, which not only reduces unaffordable memory usage and high computational cost, but also makes the network easier to train.
  arXiv  Detail & Related papers  (2020-01-14T03:34:55Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.