EAPFusion: Intrinsic Evolving Auxiliary Prior Guidance for Infrared and Visible Image Fusion

• URL: http://arxiv.org/abs/2605.01916v1
• Date: Sun, 03 May 2026 14:56:00 GMT
• Title: EAPFusion: Intrinsic Evolving Auxiliary Prior Guidance for Infrared and Visible Image Fusion
• Abstract summary: Infrared-visible image fusion aims to create an information-rich fused image by integrating the complementary thermal saliency from infrared sensing and fine textures from visible imaging.<n>Most existing fusion methods rely upon static trained weights that cannot adapt to scene-specific content at inference time.<n>We propose EAPFusion to address these issues by using self-evolving intrinsic priors instead of relying on external auxiliary models.
• Score: 15.580984590527343
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Infrared-visible image fusion aims to create an information-rich fused image by integrating the complementary thermal saliency from infrared sensing and fine textures from visible imaging. Such accurate fusion is essential for real-world perception applications in complex scenes, including nighttime autonomous driving, search and rescue, and surveillance, and can further benefit downstream tasks such as semantic segmentation. However, most existing fusion methods rely upon static trained weights that cannot adapt to scene-specific content at inference time, and often suffer from a granularity mismatch when coarse auxiliary semantics are injected, which makes it difficult to simultaneously highlight targets and preserve details. In this work, we propose EAPFusion to address these issues by using self-evolving intrinsic priors instead of relying on external auxiliary models. Concretely, EAPFusion maintains a compact set of intrinsic priors and progressively updates them across scales. These evolved priors are utilized to dynamically generate convolutional kernels, shifting the paradigm from fixed, pre-trained filters to instance-adaptive parameters via prior-conditioned dynamic convolution. Furthermore, we design a channel-level fusion module that shuffles and interleaves infrared and visible channels, applying local channel mixing to boost cross-modal complementarity. Experiments on different datasets, including cross-dataset evaluation and semantic segmentation, show that the proposed method achieves state-of-the-art quantitative and qualitative fusion results, and consistently boosts downstream performance. Code is coming soon.

Related papers

• Text-Driven Fusion for Infrared and Visible Images: Achieving Image Scene Adaptation on Hyperbolic Space [63.220767051340005]
  Infrared and visible image fusion aims to integrate complementary modalities.<n>We introduce a text-driven fusion framework empowered by hyperbolic manifold learning.<n> Experimental results show our method outperforms state-of-the-art approaches on benchmark datasets.
  arXiv  Detail & Related papers  (2026-06-13T04:33:33Z)
• Frequency-Guided Fusion For RGB-Thermal Semantic Segmentation [0.6187780920448871]
  We propose a multi-modal fusion architecture built upon dual ConvNeXt V2 backbones that employs stage-wise, modality-adaptive fusion strategies.<n>Experiments on MFNet and PST900 demonstrate that our lightest variant achieves 61.73% and 86.24% mIoU, respectively, with only 35.43M parameters.
  arXiv  Detail & Related papers  (2026-05-25T18:57:22Z)
• CtrlFuse: Mask-Prompt Guided Controllable Infrared and Visible Image Fusion [51.060328159429154]
  Infrared and visible image fusion generates all-weather perception-capable images by combining complementary modalities.<n>We propose CtrlFuse, a controllable image fusion framework that enables interactive dynamic fusion guided by mask prompts.<n> Experiments demonstrate state-of-the-art results in both fusion controllability and segmentation accuracy, with the adapted task branch even outperforming the original segmentation model.
  arXiv  Detail & Related papers  (2026-01-12T13:36:48Z)
• SGDFuse: SAM-Guided Diffusion for High-Fidelity Infrared and Visible Image Fusion [65.80051636480836]
  This paper proposes a conditional diffusion model guided by the Segment Anything Model (SAM) to achieve high-fidelity and semantically-aware image fusion.<n>The framework operates in a two-stage process: it first performs a preliminary fusion of multi-modal features, and then utilizes the semantic masks as a condition to drive the diffusion model's coarse-to-fine denoising generation.<n>Extensive experiments demonstrate that SGDFuse achieves state-of-the-art performance in both subjective and objective evaluations.
  arXiv  Detail & Related papers  (2025-08-07T10:58:52Z)
• Equivariant Multi-Modality Image Fusion [124.11300001864579]
  We propose the Equivariant Multi-Modality imAge fusion paradigm for end-to-end self-supervised learning. Our approach is rooted in the prior knowledge that natural imaging responses are equivariant to certain transformations. Experiments confirm that EMMA yields high-quality fusion results for infrared-visible and medical images.
  arXiv  Detail & Related papers  (2023-05-19T05:50:24Z)
• CoCoNet: Coupled Contrastive Learning Network with Multi-level Feature Ensemble for Multi-modality Image Fusion [68.78897015832113]
  We propose a coupled contrastive learning network, dubbed CoCoNet, to realize infrared and visible image fusion.<n>Our method achieves state-of-the-art (SOTA) performance under both subjective and objective evaluation.
  arXiv  Detail & Related papers  (2022-11-20T12:02:07Z)
• Voxel Field Fusion for 3D Object Detection [140.6941303279114]
  We present a conceptually simple framework for cross-modality 3D object detection, named voxel field fusion. The proposed approach aims to maintain cross-modality consistency by representing and fusing augmented image features as a ray in the voxel field. The framework is demonstrated to achieve consistent gains in various benchmarks and outperforms previous fusion-based methods on KITTI and nuScenes datasets.
  arXiv  Detail & Related papers  (2022-05-31T16:31:36Z)
• Infrared and Visible Image Fusion via Interactive Compensatory Attention Adversarial Learning [7.995162257955025]
  We propose a novel end-to-end mode based on generative adversarial training to achieve better fusion balance. In particular, in the generator, we construct a multi-level encoder-decoder network with a triple path, and adopt infrared and visible paths to provide additional intensity and information gradient. In addition, dual discriminators are designed to identify the similar distribution between fused result and source images, and the generator is optimized to produce a more balanced result.
  arXiv  Detail & Related papers  (2022-03-29T08:28:14Z)
• TGFuse: An Infrared and Visible Image Fusion Approach Based on Transformer and Generative Adversarial Network [15.541268697843037]
  We propose an infrared and visible image fusion algorithm based on a lightweight transformer module and adversarial learning. Inspired by the global interaction power, we use the transformer technique to learn the effective global fusion relations. The experimental performance demonstrates the effectiveness of the proposed modules, with superior improvement against the state-of-the-art.
  arXiv  Detail & Related papers  (2022-01-25T07:43:30Z)

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.