Fusion of Infrared and Visible Images based on Spatial-Channel Attentional Mechanism

• URL: http://arxiv.org/abs/2308.13672v1
• Date: Fri, 25 Aug 2023 21:05:11 GMT
• Title: Fusion of Infrared and Visible Images based on Spatial-Channel Attentional Mechanism
• Authors: Qian Xu
• Abstract summary: We present AMFusionNet, an innovative approach to infrared and visible image fusion (IVIF) By assimilating thermal details from infrared images with texture features from visible sources, our method produces images enriched with comprehensive information. Our method outperforms state-of-the-art algorithms in terms of quality and quantity.
• Score: 3.388001684915793
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In the study, we present AMFusionNet, an innovative approach to infrared and visible image fusion (IVIF), harnessing the power of multiple kernel sizes and attention mechanisms. By assimilating thermal details from infrared images with texture features from visible sources, our method produces images enriched with comprehensive information. Distinct from prevailing deep learning methodologies, our model encompasses a fusion mechanism powered by multiple convolutional kernels, facilitating the robust capture of a wide feature spectrum. Notably, we incorporate parallel attention mechanisms to emphasize and retain pivotal target details in the resultant images. Moreover, the integration of the multi-scale structural similarity (MS-SSIM) loss function refines network training, optimizing the model for IVIF task. Experimental results demonstrate that our method outperforms state-of-the-art algorithms in terms of quality and quantity. The performance metrics on publicly available datasets also show significant improvement

Related papers

• Multi-Head Attention Residual Unfolded Network for Model-Based Pansharpening [2.874893537471256]
  Unfolding fusion methods integrate the powerful representation capabilities of deep learning with the robustness of model-based approaches. In this paper, we propose a model-based deep unfolded method for satellite image fusion. Experimental results on PRISMA, Quickbird, and WorldView2 datasets demonstrate the superior performance of our method.
  arXiv  Detail & Related papers  (2024-09-04T13:05:00Z)
• SSPFusion: A Semantic Structure-Preserving Approach for Infrared and Visible Image Fusion [30.55433673796615]
  Most existing learning-based infrared and visible image fusion (IVIF) methods exhibit massive redundant information in the fusion images. We propose a semantic structure-preserving approach for IVIF, namely SSPFusion. Our method is able to generate high-quality fusion images from pairs of infrared and visible images, which can boost the performance of downstream computer-vision tasks.
  arXiv  Detail & Related papers  (2023-09-26T08:13:32Z)
• An Interactively Reinforced Paradigm for Joint Infrared-Visible Image Fusion and Saliency Object Detection [59.02821429555375]
  This research focuses on the discovery and localization of hidden objects in the wild and serves unmanned systems. Through empirical analysis, infrared and visible image fusion (IVIF) enables hard-to-find objects apparent. multimodal salient object detection (SOD) accurately delineates the precise spatial location of objects within the picture.
  arXiv  Detail & Related papers  (2023-05-17T06:48:35Z)
• CDDFuse: Correlation-Driven Dual-Branch Feature Decomposition for Multi-Modality Image Fusion [138.40422469153145]
  We propose a novel Correlation-Driven feature Decomposition Fusion (CDDFuse) network. We show that CDDFuse achieves promising results in multiple fusion tasks, including infrared-visible image fusion and medical image fusion.
  arXiv  Detail & Related papers  (2022-11-26T02:40:28Z)
• CoCoNet: Coupled Contrastive Learning Network with Multi-level Feature Ensemble for Multi-modality Image Fusion [72.8898811120795]
  We propose a coupled contrastive learning network, dubbed CoCoNet, to realize infrared and visible image fusion. Our method achieves state-of-the-art (SOTA) performance under both subjective and objective evaluation.
  arXiv  Detail & Related papers  (2022-11-20T12:02:07Z)
• Interactive Feature Embedding for Infrared and Visible Image Fusion [94.77188069479155]
  General deep learning-based methods for infrared and visible image fusion rely on the unsupervised mechanism for vital information retention. We propose a novel interactive feature embedding in self-supervised learning framework for infrared and visible image fusion.
  arXiv  Detail & Related papers  (2022-11-09T13:34:42Z)
• Image-specific Convolutional Kernel Modulation for Single Image Super-resolution [85.09413241502209]
  In this issue, we propose a novel image-specific convolutional modulation kernel (IKM) We exploit the global contextual information of image or feature to generate an attention weight for adaptively modulating the convolutional kernels. Experiments on single image super-resolution show that the proposed methods achieve superior performances over state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-11-16T11:05:10Z)
• Image super-resolution reconstruction based on attention mechanism and feature fusion [3.42658286826597]
  A network structure based on attention mechanism and multi-scale feature fusion is proposed. Experimental results show that the proposed method can achieve better performance over other representative super-resolution reconstruction algorithms.
  arXiv  Detail & Related papers  (2020-04-08T11:20:10Z)
• Learning Enriched Features for Real Image Restoration and Enhancement [166.17296369600774]
  convolutional neural networks (CNNs) have achieved dramatic improvements over conventional approaches for image restoration task. We present a novel architecture with the collective goals of maintaining spatially-precise high-resolution representations through the entire network. Our approach learns an enriched set of features that combines contextual information from multiple scales, while simultaneously preserving the high-resolution spatial details.
  arXiv  Detail & Related papers  (2020-03-15T11:04:30Z)

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.