SeACPFusion:基于亮度感知的红外和可见光图像自适应融合网络

IF 3.1 3区 物理与天体物理 Q2 INSTRUMENTS & INSTRUMENTATION Infrared Physics & Technology Pub Date : 2024-08-30 DOI:10.1016/j.infrared.2024.105541
Wangjie Li , Xiaoyi Lv , Yaoyong Zhou , Yunling Wang , Min Li
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引用次数: 0

摘要

生成能突出重要目标并保留纹理细节的单一融合图像是融合可见光和红外图像的目的。目前使用的大多数基于深度学习的融合算法都能产生不错的融合结果,但在建模过程中仍然缺乏对不同场景或区域中不同信息量的考虑。因此,我们在本研究中提出了 SeACPFusion,一种亮度感知的红外和可见光图像自适应融合网络,它能以最佳比例自适应地保留源图像中显著目标的强度信息和背景的纹理信息。具体来说,我们设计了像素级亮度损失(PBL)来指导融合模型的实时训练,PBL 可根据不同源图像的像素亮度比保留最佳强度信息。此外,我们还设计了通道变换器(CTF),从特征通道的角度考虑不同属性之间的关系,利用自聚焦机制聚焦关键信息,实现自适应融合的目标。我们在 MSRS、RoadScene 和 TNO 数据集上进行的大量测试表明,SeACPFusion 在六项客观指标上超越了九种具有代表性的深度学习方法,并在曝光过度或曝光不足等场景中实现了最佳视觉效果。此外,相对高效的运行和较少的模型参数使我们的算法有望成为下游复杂视觉任务的预处理模块。
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SeACPFusion: An Adaptive Fusion Network for Infrared and Visible Images based on brightness perception

Generating a single fused image that highlights important targets and preserves textural details is the aim of fusing visible and infrared images. The majority of deep learning-based fusion algorithms now in use can produce decent fusion outcomes; however, the modeling process still lacks consideration of the different amounts of information in different scenes or regions. Thus, we propose in this research SeACPFusion, a luminance-aware adaptive fusion network for infrared and visible images, which adaptively preserves the intensity information of the noticeable targets of the source images with the texture information of the background in an optimal ratio. Specifically, we design pixel-level luminance loss (PBL) to direct the fusion model’s training in real-time, and PBL retains the optimal intensity information according to the pixel luminance ratio of different source images. In addition, we designed the Channel Transformer (CTF) to consider the relationship between different attributes from the point of view of the feature channel and to focus on the key information by using the self-focusing mechanism to achieve the goal of adaptive fusion. Our extensive tests on the MSRS, RoadScene, and TNO datasets demonstrate that SeACPFusion surpasses nine representative deep learning methods on six objective metrics and achieves the best visual results in scenes such as overexposure or underexposure. In addition, the relatively efficient operation and fewer model parameters make our algorithm promising as a preprocessing module for downstream complicated vision 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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