ConvNeXtFusion:利用残差密集和交叉ConvNeXt网络进行多传感器图像融合

IF 3.4 3区 物理与天体物理 Q2 INSTRUMENTS & INSTRUMENTATION
Mohammed Zouaoui Laidouni, Boban Bondžulić, Dimitrije Bujaković, Touati Adli, Milenko Andrić
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引用次数: 0

摘要

多传感器图像融合通过将不同图像形态的信息融合在一起产生单一的融合图像来增强视觉感知能力。本文提出了一种基于ConvNeXt网络的多传感器图像融合框架,融合可见光(VIS)和长波红外(LWIR)图像,并具有融合近红外(NIR)图像的附加能力。该框架引入了残差密集ConvNeXt模块,专门用于跨不同模态的密集特征提取。为了进一步优化融合过程,开发了残差交叉ConvNeXt模块来组合提取的特征。因此,最大化模式之间的互动,并导致更多的信息融合图像。为了便于无监督训练并保证融合图像中组合模态的准确表示,构造了一个积分频率和梯度信息的损失函数。通过在四个不同的数据集上的实验,包括主观评价和客观比较,广泛验证了所提出的方法。结果表明,该框架优于现有最先进的图像融合算法,特别是在处理LWIR+VIS和LWIR+NIR+VIS融合任务方面具有强大的泛化能力。最后,通过对目标检测任务的应用,进一步证明了该方法的实用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

ConvNeXtFusion: Multi-sensor image fusion via residual dense and cross ConvNeXt network

ConvNeXtFusion: Multi-sensor image fusion via residual dense and cross ConvNeXt network
Multi-sensor image fusion enhances the visual perception ability by producing a single fused image that combines the information from different image modalities. This paper presents a novel framework for multi-sensor image fusion based on ConvNeXt network to fuse visible (VIS) and long-wavelength infrared (LWIR) images, with the additional capability to incorporate near-infrared (NIR) image. The framework introduces a residual dense ConvNeXt module specifically designed for dense feature extraction across different modalities. To further optimize the fusion process, a residual cross ConvNeXt module is developed to combine the extracted features. Therefore, maximizing the interaction between modalities and leading to a more informative fused image. To facilitate unsupervised training and ensure the accurate representation of combined modalities in the fused image, a loss function integrating frequency and gradient information is constructed. The proposed method is extensively validated through experiments on four distinct datasets, including both subjective evaluations and objective comparisons. The results demonstrate the proposed framework’s superiority over existing state-of-the-art image fusion algorithms, particularly highlighting its strong generalization capability in handling both LWIR+VIS and LWIR+NIR+VIS fusion tasks. Finally, the practical utility of the proposed method is further demonstrated through its application to object detection tasks.
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来源期刊
CiteScore
5.70
自引率
12.10%
发文量
400
审稿时长
67 days
期刊介绍: The Journal covers the entire field of infrared physics and technology: theory, experiment, application, devices and instrumentation. Infrared'' is defined as covering the near, mid and far infrared (terahertz) regions from 0.75um (750nm) to 1mm (300GHz.) Submissions in the 300GHz to 100GHz region may be accepted at the editors discretion if their content is relevant to shorter wavelengths. Submissions must be primarily concerned with and directly relevant to this spectral region. Its core topics can be summarized as the generation, propagation and detection, of infrared radiation; the associated optics, materials and devices; and its use in all fields of science, industry, engineering and medicine. Infrared techniques occur in many different fields, notably spectroscopy and interferometry; material characterization and processing; atmospheric physics, astronomy and space research. Scientific aspects include lasers, quantum optics, quantum electronics, image processing and semiconductor physics. Some important applications are medical diagnostics and treatment, industrial inspection and environmental monitoring.
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