UniFusion: A Unified Image Fusion Framework with Robust Representation and Source-Aware Preservation

• URL: http://arxiv.org/abs/2603.14214v1
• Date: Sun, 15 Mar 2026 04:07:06 GMT
• Title: UniFusion: A Unified Image Fusion Framework with Robust Representation and Source-Aware Preservation
• Abstract summary: We propose UniFusion, a unified image fusion framework to achieve cross-task generalization.<n>We introduce a reconstruction-alignment loss to maintain consistency between fused outputs and inputs.<n>Experiments across multiple fusion tasks demonstrate UniFusion's superior visual quality, generalization ability, and adaptability to real-world scenarios.
• Score: 18.352691348247294
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Image fusion aims to integrate complementary information from multiple source images to produce a more informative and visually consistent representation, benefiting both human perception and downstream vision tasks. Despite recent progress, most existing fusion methods are designed for specific tasks (i.e., multi-modal, multi-exposure, or multi-focus fusion) and struggle to effectively preserve source information during the fusion process. This limitation primarily arises from task-specific architectures and the degradation of source information caused by deep-layer propagation. To overcome these issues, we propose UniFusion, a unified image fusion framework designed to achieve cross-task generalization. First, leveraging DINOv3 for modality-consistent feature extraction, UniFusion establishes a shared semantic space for diverse inputs. Second, to preserve the understanding of each source image, we introduce a reconstruction-alignment loss to maintain consistency between fused outputs and inputs. Finally, we employ a bilevel optimization strategy to decouple and jointly optimize reconstruction and fusion objectives, effectively balancing their coupling relationship and ensuring smooth convergence. Extensive experiments across multiple fusion tasks demonstrate UniFusion's superior visual quality, generalization ability, and adaptability to real-world scenarios. Code is available at https://github.com/dusongcheng/UniFusion.

Related papers

• UniDiffFusion: A Unified Diffusion Framework for Multi-Task and Degradation-Robust Image Fusion [14.759665227535173]
  General image fusion aims to integrate complementary information from multiple source images.<n>Existing methods often rely on task-specific models and struggle to maintain robust performance under diverse degradation conditions.<n>We propose UniDiffFusion, a unified diffusion framework for multi-task and degradation-robust image fusion.
  arXiv  Detail & Related papers  (2026-08-22T05:36:59Z)
• Towards Unified Semantic and Controllable Image Fusion: A Diffusion Transformer Approach [99.80480649258557]
  DiTFuse is an instruction-driven framework that performs semantics-aware fusion within a single model.<n>Experiments on public IVIF, MFF, and MEF benchmarks confirm superior quantitative and qualitative performance, sharper textures, and better semantic retention.
  arXiv  Detail & Related papers  (2025-12-08T05:04:54Z)
• 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)
• Fusion from Decomposition: A Self-Supervised Approach for Image Fusion and Beyond [74.96466744512992]
  The essence of image fusion is to integrate complementary information from source images. DeFusion++ produces versatile fused representations that can enhance the quality of image fusion and the effectiveness of downstream high-level vision tasks.
  arXiv  Detail & Related papers  (2024-10-16T06:28:49Z)
• From Text to Pixels: A Context-Aware Semantic Synergy Solution for Infrared and Visible Image Fusion [66.33467192279514]
  We introduce a text-guided multi-modality image fusion method that leverages the high-level semantics from textual descriptions to integrate semantics from infrared and visible images. Our method not only produces visually superior fusion results but also achieves a higher detection mAP over existing methods, achieving state-of-the-art results.
  arXiv  Detail & Related papers  (2023-12-31T08:13:47Z)
• TextFusion: Unveiling the Power of Textual Semantics for Controllable Image Fusion [38.61215361212626]
  We propose a text-guided fusion paradigm for advanced image fusion. We release a text-annotated image fusion dataset IVT. Our approach consistently outperforms traditional appearance-based fusion methods.
  arXiv  Detail & Related papers  (2023-12-21T09:25:10Z)
• A Task-guided, Implicitly-searched and Meta-initialized Deep Model for Image Fusion [69.10255211811007]
  We present a Task-guided, Implicit-searched and Meta- generalizationd (TIM) deep model to address the image fusion problem in a challenging real-world scenario. Specifically, we propose a constrained strategy to incorporate information from downstream tasks to guide the unsupervised learning process of image fusion. Within this framework, we then design an implicit search scheme to automatically discover compact architectures for our fusion model with high efficiency.
  arXiv  Detail & Related papers  (2023-05-25T08:54:08Z)
• 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)
• TransFuse: A Unified Transformer-based Image Fusion Framework using Self-supervised Learning [5.849513679510834]
  Image fusion is a technique to integrate information from multiple source images with complementary information to improve the richness of a single image. Two-stage methods avoid the need of large amount of task-specific training data by training encoder-decoder network on large natural image datasets. We propose a destruction-reconstruction based self-supervised training scheme to encourage the network to learn task-specific features.
  arXiv  Detail & Related papers  (2022-01-19T07:30:44Z)

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.