Task-driven Image Fusion with Learnable Fusion Loss

• URL: http://arxiv.org/abs/2412.03240v1
• Date: Wed, 04 Dec 2024 11:42:17 GMT
• Title: Task-driven Image Fusion with Learnable Fusion Loss
• Authors: Haowen Bai, Jiangshe Zhang, Zixiang Zhao, Yichen Wu, Lilun Deng, Yukun Cui, Tao Feng, Shuang Xu,
• Abstract summary: Task-driven Image Fusion (TDFusion) is a fusion framework incorporating a learnable fusion loss guided by task loss. Experiments demonstrate TDFusion's performance in both fusion and task-related applications.
• Score: 18.840276588000155
• License:
• Abstract: Multi-modal image fusion aggregates information from multiple sensor sources, achieving superior visual quality and perceptual characteristics compared to any single source, often enhancing downstream tasks. However, current fusion methods for downstream tasks still use predefined fusion objectives that potentially mismatch the downstream tasks, limiting adaptive guidance and reducing model flexibility. To address this, we propose Task-driven Image Fusion (TDFusion), a fusion framework incorporating a learnable fusion loss guided by task loss. Specifically, our fusion loss includes learnable parameters modeled by a neural network called the loss generation module. This module is supervised by the loss of downstream tasks in a meta-learning manner. The learning objective is to minimize the task loss of the fused images, once the fusion module has been optimized by the fusion loss. Iterative updates between the fusion module and the loss module ensure that the fusion network evolves toward minimizing task loss, guiding the fusion process toward the task objectives. TDFusion's training relies solely on the loss of downstream tasks, making it adaptable to any specific task. It can be applied to any architecture of fusion and task networks. Experiments demonstrate TDFusion's performance in both fusion and task-related applications, including four public fusion datasets, semantic segmentation, and object detection. The code will be released.

Related papers

• 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)
• ReFusion: Learning Image Fusion from Reconstruction with Learnable Loss via Meta-Learning [17.173784980297214]
  ReFusion is a unified meta-learning based image fusion framework. It dynamically optimize the fusion loss for various tasks through source image reconstruction. It is capable of adapting to various tasks, including infrared-visible, medical, multi-focus, and multi-exposure image fusion.
  arXiv  Detail & Related papers  (2023-12-13T07:40:39Z)
• Mutual-Guided Dynamic Network for Image Fusion [51.615598671899335]
  We propose a novel mutual-guided dynamic network (MGDN) for image fusion, which allows for effective information utilization across different locations and inputs. Experimental results on five benchmark datasets demonstrate that our proposed method outperforms existing methods on four image fusion tasks.
  arXiv  Detail & Related papers  (2023-08-24T03:50:37Z)
• 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)
• 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)
• LRRNet: A Novel Representation Learning Guided Fusion Network for Infrared and Visible Images [98.36300655482196]
  We formulate the fusion task mathematically, and establish a connection between its optimal solution and the network architecture that can implement it. In particular we adopt a learnable representation approach to the fusion task, in which the construction of the fusion network architecture is guided by the optimisation algorithm producing the learnable model. Based on this novel network architecture, an end-to-end lightweight fusion network is constructed to fuse infrared and visible light images.
  arXiv  Detail & Related papers  (2023-04-11T12:11:23Z)
• Breaking Free from Fusion Rule: A Fully Semantic-driven Infrared and Visible Image Fusion [51.22863068854784]
  Infrared and visible image fusion plays a vital role in the field of computer vision. Previous approaches make efforts to design various fusion rules in the loss functions. We develop a semantic-level fusion network to sufficiently utilize the semantic guidance.
  arXiv  Detail & Related papers  (2022-11-22T13:59:59Z)
• RFN-Nest: An end-to-end residual fusion network for infrared and visible images [37.935940961760785]
  We propose an end-to-end fusion network architecture (RFN-Nest) for infrared and visible image fusion. A novel detail-preserving loss function, and a feature enhancing loss function are proposed to train RFN. The experimental results on public domain data sets show that, compared with the existing methods, our end-to-end fusion network delivers a better performance than the state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-03-07T07:29:50Z)

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.