School of Information Engineering, Southwest University of Science and Technology, Mianyang, Sichuan, China.
School of Computer Science and Engineering, Sichuan University of Science and Engineering, Zigong, Sichuan, China.
PLoS One. 2023 Aug 2;18(8):e0286024. doi: 10.1371/journal.pone.0286024. eCollection 2023.
Image fusion technology is employed to integrate images collected by utilizing different types of sensors into the same image to generate high-definition images and extract more comprehensive information. However, all available techniques derive the features of the images by utilizing each sensor separately, resulting in poorly correlated image features when different types of sensors are utilized during the fusion process. The fusion strategy to make up for the differences between features alone is an important reason for the poor clarity of fusion results. Therefore, this paper proposes a fusion method via information clustering and image features (ICIF). First, the weighted median filter algorithm is adopted in the spatial domain to realize the clustering of images, which uses the texture features of an infrared image as the weight to influence the clustering results of the visible light image. Then, the image is decomposed into the base layer, bright detail layer, and dark detail layer, which improves the correlations between the layers after conducting the decomposition of a source graph. Finally, the characteristics of the images collected by utilizing sensors and feature information between the image layers are used as the weight reference of the fusion strategy. Hence, the fusion images are reconstructed according to the principle of extended texture details. Experiments on public datasets demonstrate the superiority of the proposed strategy over state-of-the-art methods. The proposed ICIF highlighted targets and abundant details as well. Moreover, we also generalize the proposed ICIF to fuse images with different sensors, e.g., medical images and multi-focus images.
图像融合技术用于将利用不同类型传感器采集的图像集成到同一幅图像中，以生成高清图像并提取更全面的信息。然而，所有可用的技术都是通过单独利用每个传感器来提取图像的特征，因此在融合过程中使用不同类型的传感器时，图像特征相关性较差。仅通过融合策略来弥补特征之间的差异，是导致融合结果清晰度较差的一个重要原因。因此，本文提出了一种基于信息聚类和图像特征的融合方法（ICIF）。首先，在空间域中采用加权中值滤波算法实现图像聚类，利用红外图像的纹理特征作为权重来影响可见光图像的聚类结果。然后，将图像分解为基础层、亮细节层和暗细节层，在对源图进行分解后，提高了各层之间的相关性。最后，利用传感器采集的图像特征和图像层之间的特征信息作为融合策略的权重参考。根据扩展纹理细节的原理对融合图像进行重构。在公共数据集上的实验表明，所提出的策略优于最先进的方法。所提出的 ICIF 突出了目标和丰富的细节。此外，我们还将所提出的 ICIF 推广到融合不同传感器的图像，例如医学图像和多焦点图像。
