Ancient murals have often suffered from blurring and loss of line structures over time, making their interpretation challenging. Hyperspectral imaging technology, capable of capturing subtle variations in material and energy, provides valuable information for enhancing these faint or missing details. Therefore, this paper proposes a method for enhancing mural line information that combines the wavelet transform with hyperspectral imaging data. Firstly, the dimensionality of the mural hyperspectral image was reduced by using the Minimum Noise Fraction (MNF) transform. The optimal MNF band image was selected using the maximum average gradient method. The MNF results were used to extract the pure end members, and the corresponding abundance maps were inverted by fully constrained least squares spectral unmixing. The line abundance map was combined with the optimal MNF band image through band operations to obtain a linear feature-enhanced image. Then, the true color image was synthesized using the inverse MNF transformation. Both the linear feature-enhanced image and the true color image were processed with Gaussian filtering to enhance detail. Haar wavelet decomposition was applied to both images. The corresponding high-frequency components were fused, while the low-frequency component from the true color image was retained to reconstruct the final color image with enhanced line features. The experimental validation on murals from Yiju Temple (Shanxi) shows that, in comparison with a PCA-based enhancement technique, the proposed approach achieves an increase of 0.083 7 in average gradient and 15.253 1 in edge intensity, indicating more effective enhancement of line features and offering valuable insights for mural preservation and restoration efforts.