We propose a dual-band fusion method using multiscale visual saliency extraction based on spatial weight matrix. There are two main contributions in this paper. The first major contribution is to use the local window based on the gray distance of the spatial weight for saliency extraction. Second, to emphasize those potential targets and details with different sizes from source images, we based on the saliency extraction method of multi-window and fusion of different scales to achieve the preservation of more information. The proposed method is mainly divided into four steps. First, we use a spatial weight matrix of different window sizes to enhance targets of different sizes in the image. Then, through the different processing of each enhanced image to get detailed images, we use a special method to fuse the obtained details of each scale. Then, reconstruct the results obtained at each scale. Finally, we have the exact weight index selection to get better fusion results. This method solves the problem of improper weight selection and the result deteriorates. Through comparison and verification, our results retain more detailed information from the original images.

中文翻译：

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基于空间权重矩阵的多尺度视觉显着性提取红外与可见图像融合

我们提出了一种基于空间权重矩阵的多尺度视觉显着性提取的双频带融合方法。本文有两个主要贡献。第一个主要贡献是将基于空间权重的灰色距离的局部窗口用于显着性提取。其次，为了强调源图像中具有不同大小的潜在目标和细节，我们基于多窗口的显着性提取方法和不同尺度的融合来实现更多信息的保存。所提出的方法主要分为四个步骤。首先，我们使用不同窗口大小的空间权重矩阵来增强图像中不同大小的目标。然后，通过对每个增强图像进行不同的处理以获得细节图像，我们使用一种特殊的方法来融合所获得的每个比例尺的细节。然后，重建在每个尺度上获得的结果。最后，我们可以选择精确的权重指数以获得更好的融合结果。该方法解决了重量选择不当的问题，结果恶化了。通过比较和验证，我们的结果保留了原始图像中的更多详细信息。