# Research on Multi-Scale Fusion Method for Ancient Bronze Ware X-ray Images in NSST Domain
###### Abstract
This paper draws on medical image fusion, as well as infrared and visible light image fusion techniques to process the bronze X-ray images. To observe all the information present on bronze mirrors simultaneously, enhancing the expression of the bronze mirror decorations and highlighting the defective areas of the mirrors through image fusion techniques is necessary. However, achieving such an effect utilizing the traditional MST method proves challenging, so some scholars have improved the fusion rule. [PERSON] et al. [9] proposed an NSCT-based fusion technique that incorporates the local Laplace operator and phase consistency for enhanced fusion performance, but it results in the loss of some fusion details. [PERSON] et al. [10] proposed an image fusion method based on multi-decomposition LatLRR. The method fuses images after decomposing them several times with LatLRR, which improves the brightness while preserving the detail information. However, it is worth noting that in some cases, the fused images may exhibit overly excessive brightness. [PERSON] et al. [11] proposed a method for image fusion that combines NSCT and adaptive PCNN. The fusion rules utilize the sum of directional and global gradients, leading to improved method timeliness. However, it is important to note that a potential disadvantage of this method is that some fused images may exhibit subtle blurring. [PERSON] et al. [12] proposed a spatial frequency excitation-based PA-PCNN fusion method in the NSST domain. The method utilizes a maxima strategy for the low-frequency subband coefficients. However, it is important to note that a potential drawback of this method is that the resulting image contrast may not be sufficient, leading to a loss of energy and detail information. [PERSON] et al. [13] proposed the NSST-based PAULPCNN fusion method to fuse significant complementary details in grayscale images with pseudo-color images. This method can extract rich information and is effective in medical image fusion. However, these methods still have more defects in terms of image quality and detail preservation in bronze mirrors. In order to solve this problem, this paper undertakes the task of introducing a multi-scale morphological gradient operator combined with a WSEML operator in the NSST domain for high-frequency information fusion. The Laplace operator, a second-order differential operator, is highly sensitive to grayscale variations in images, making it ideal for accentuating areas with rapid changes. Building on this, the multi-scale morphological gradient operator adjusts weight coefficients to refine the geometric relationships among eight neighboring pixels, thereby enhancing edge detection accuracy and mitigating noise impact. For the low-frequency components, fusion is achieved through a locally topologically coupled neural P system that leverages nonlinear modulation and dynamic thresholds to preserve the image's overall integrity and detail. The results demonstrate the optimal performance in the evaluation metrics of Average Gradient, Mutual Information, Gradient-based Fusion Performance, and visual information fidelity, with an average improvement of about 4%. Visual information fidelity is more prominent, increasing by 6.2%.