Computer Science and Engineering, Sichuan University of Science and Engineering, Yibin, Sichuan, China.
Grassland Research Institute, Xinjiang Academy of Animal Sciences, Urumqi, Xinjiang, China.
PLoS One. 2022 Dec 30;17(12):e0278055. doi: 10.1371/journal.pone.0278055. eCollection 2022.
Multi-scale image decomposition is crucial for image fusion, extracting prominent feature textures from infrared and visible light images to obtain clear fused images with more textures. This paper proposes a fusion method of infrared and visible light images based on spatial domain and image features to obtain high-resolution and texture-rich images. First, an efficient hierarchical image clustering algorithm based on superpixel fast pixel clustering directly performs multi-scale decomposition of each source image in the spatial domain and obtains high-frequency, medium-frequency, and low-frequency layers to extract the maximum and minimum values of each source image combined images. Then, using the attribute parameters of each layer as fusion weights, high-definition fusion images are through adaptive feature fusion. Besides, the proposed algorithm performs multi-scale decomposition of the image in the spatial frequency domain to solve the information loss problem caused by the conversion process between the spatial frequency and frequency domains in the traditional extraction of image features in the frequency domain. Eight image quality indicators are compared with other fusion algorithms. Experimental results show that this method outperforms other comparative methods in both subjective and objective measures. Furthermore, the algorithm has high definition and rich textures.
多尺度图像分解对于图像融合至关重要，它可以从红外和可见光图像中提取突出的特征纹理，从而获得具有更多纹理的清晰融合图像。本文提出了一种基于空域和图像特征的红外与可见光图像融合方法，以获得高分辨率和富含纹理的图像。首先，提出了一种基于超像素快速像素聚类的高效分层图像聚类算法，直接在空域对各源图像进行多尺度分解，得到高频、中频和低频层，提取各源图像结合图像的最大值和最小值。然后，利用各层的属性参数作为融合权重，通过自适应特征融合得到高清晰度融合图像。此外，该算法在空域频域进行图像多尺度分解，解决了传统频域图像特征提取中在空域和频域之间转换过程中信息丢失的问题。与其他融合算法相比，使用了 8 个图像质量指标进行比较。实验结果表明，该方法在主观和客观评价方面均优于其他比较方法。此外，该算法具有高清晰度和丰富的纹理。
