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DenseFuse: A Fusion Approach to Infrared and Visible Images

arXiv 1804.08361 ↗First posted 23 Apr 2018cs.CVcited by 180

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Abstract (arXiv)

In this paper, we present a novel deep learning architecture for infrared and visible images fusion problem. In contrast to conventional convolutional networks, our encoding network is combined by convolutional layers, fusion layer and dense block in which the output of each layer is connected to every other layer. We attempt to use this architecture to get more useful features from source images in encoding process. And two fusion layers(fusion strategies) are designed to fuse these features. Finally, the fused image is reconstructed by decoder. Compared with existing fusion methods, the proposed fusion method achieves state-of-the-art performance in objective and subjective assessment. Code and pre-trained models are available at https://github.com/hli1221/imagefusion_densefuse

Key facts. Cited by 180 papers in the map (data through 7 Oct 2026). First posted on arXiv 23 Apr 2018.

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Citing papers per year: 2021: 16; 2022: 21; 2023: 32; 2024: 39; 2025: 42; 2026: 30.

180 papers.

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  1. 2022Target-aware Dual Adversarial Learning and a Multi-scenario Multi-Modality Benchmark to Fuse Infrared and Visible for Object Detectioncited by 313
  2. 2021LLVIP: A Visible-infrared Paired Dataset for Low-light Visioncited by 229
  3. 2022CDDFuse: Correlation-Driven Dual-Branch Feature Decomposition for Multi-Modality Image Fusioncited by 222
  4. 2023DDFM: Denoising Diffusion Model for Multi-Modality Image Fusioncited by 153
  5. 2023Multi-interactive Feature Learning and a Full-time Multi-modality Benchmark for Image Fusion and Segmentationcited by 128
  6. 2021RFN-Nest: An end-to-end residual fusion network for infrared and visible imagescited by 109
  7. 2023Equivariant Multi-Modality Image Fusioncited by 98
  8. 2023LRRNet: A Novel Representation Learning Guided Fusion Network for Infrared and Visible Imagescited by 87
  9. 2024FusionMamba: Dynamic Feature Enhancement for Multimodal Image Fusion with Mambacited by 81
  10. 2022Unsupervised Misaligned Infrared and Visible Image Fusion via Cross-Modality Image Generation and Registrationcited by 80
  11. 2024Multimodal Alignment and Fusion: A Surveycited by 73
  12. 2024Fusion-Mamba for Cross-modality Object Detectioncited by 67
38 more
  1. 2022CoCoNet: Coupled Contrastive Learning Network with Multi-level Feature Ensemble for Multi-modality Image Fusioncited by 63
  2. 2024Task-Customized Mixture of Adapters for General Image Fusioncited by 59
  3. 2023Dif-Fusion: Towards High Color Fidelity in Infrared and Visible Image Fusion with Diffusion Modelscited by 56
  4. 2023Multi-modal Gated Mixture of Local-to-Global Experts for Dynamic Image Fusioncited by 54
  5. 2024Multimodal Fusion on Low-quality Data: A Comprehensive Surveycited by 48
  6. 2023A Task-guided, Implicitly-searched and Meta-initialized Deep Model for Image Fusioncited by 47
  7. 2021Deep Learning for HDR Imaging: State-of-the-Art and Future Trendscited by 45
  8. 2024Image Fusion via Vision-Language Modelcited by 40
  9. 2024CrossFuse: A Novel Cross Attention Mechanism based Infrared and Visible Image Fusion Approachcited by 38
  10. 2023An Interactively Reinforced Paradigm for Joint Infrared-Visible Image Fusion and Saliency Object Detectioncited by 36
  11. 2024MambaDFuse: A Mamba-based Dual-phase Model for Multi-modality Image Fusioncited by 35
  12. 2025DCEvo: Discriminative Cross-Dimensional Evolutionary Learning for Infrared and Visible Image Fusioncited by 34
  13. 2021TransMEF: A Transformer-Based Multi-Exposure Image Fusion Framework using Self-Supervised Multi-Task Learningcited by 32
  14. 2024Text-DiFuse: An Interactive Multi-Modal Image Fusion Framework based on Text-modulated Diffusion Modelcited by 31
  15. 2022TGFuse: An Infrared and Visible Image Fusion Approach Based on Transformer and Generative Adversarial Networkcited by 29
  16. 2023Learning a Graph Neural Network with Cross Modality Interaction for Image Fusioncited by 28
  17. 2023Bi-level Dynamic Learning for Jointly Multi-modality Image Fusion and Beyondcited by 26
  18. 2023PAIF: Perception-Aware Infrared-Visible Image Fusion for Attack-Tolerant Semantic Segmentationcited by 26
  19. 2023TextFusion: Unveiling the Power of Textual Semantics for Controllable Image Fusioncited by 26
  20. 2022SwinFuse: A Residual Swin Transformer Fusion Network for Infrared and Visible Imagescited by 25
  21. 2021Image Fusion Transformercited by 24
  22. 2024Task-driven Image Fusion with Learnable Fusion Losscited by 24
  23. 2023From Text to Pixels: A Context-Aware Semantic Synergy Solution for Infrared and Visible Image Fusioncited by 19
  24. 2023Improving Misaligned Multi-modality Image Fusion with One-stage Progressive Dense Registrationcited by 17
  25. 2023ReFusion: Learning Image Fusion from Reconstruction with Learnable Loss via Meta-Learningcited by 15
  26. 2024MaeFuse: Transferring Omni Features with Pretrained Masked Autoencoders for Infrared and Visible Image Fusion via Guided Trainingcited by 15
  27. 2022D2HNet: Joint Denoising and Deblurring with Hierarchical Network for Robust Night Image Restorationcited by 14
  28. 2024Conditional Controllable Image Fusioncited by 13
  29. 2023FusionBooster: A Unified Image Fusion Boosting Paradigmcited by 11
  30. 2024MMDRFuse: Distilled Mini-Model with Dynamic Refresh for Multi-Modality Image Fusioncited by 10
  31. 2024Infrared and Visible Image Fusion with Language-Driven Loss in CLIP Embedding Spacecited by 9
  32. 2024Rethinking Early-Fusion Strategies for Improved Multispectral Object Detectioncited by 9
  33. 2025LUT-Fuse: Towards Extremely Fast Infrared and Visible Image Fusion via Distillation to Learnable Look-Up Tablescited by 9
  34. 2021A Dual-branch Network for Infrared and Visible Image Fusioncited by 8
  35. 2024Pedestrian Detection in Low-Light Conditions: A Comprehensive Surveycited by 8
  36. 2024S4Fusion: Saliency-aware Selective State Space Model for Infrared Visible Image Fusioncited by 8
  37. 2025ControlFusion: A Controllable Image Fusion Framework with Language-Vision Degradation Promptscited by 8
  38. 2025A Unified Solution to Video Fusion: From Multi-Frame Learning to Benchmarkingcited by 8

50 most cited of 180 All 180 (JSON)