The study of linear features plays an important role in the field of mural protection and restoration. However, due to the natural environment or other factors, murals often suffer from many diseases which make identification of their linear features difficult. Therefore, in this study, a method combining minimum noise fraction (MNF) and Haar wavelet transform was proposed to enhance the linear features using hyperspectral images of murals. Firstly, MNF transformation was carried out on the hyperspectral images, and then the top 10 bands were selected for inverse MNF transformation for reconstruction to reduce noise of the hyperspectral image. Secondly, true color bands of the reconstructed image were transformed into a gray image, which was decomposed by Haar wavelet in the next step. Then the optimal band of the image after MNF was chosen by using the maximum average gradient algorithm. It was transformed into two parts—a low-frequency signal and high-frequency signal—by the same Haar wavelet as above. After that, this low-frequency signal part was fused with the low-frequency signal part from the gray image decomposition to get the optimized low-frequency signal. The reconstructed gray image above is considered to be the optimized high-frequency signal. Finally, the optimized low and high frequency signals were transformed inversely to the resultant image by the Haar wavelet. Comparison of the original gray image and resulting enhancement by principal component analysis, verifies the effectiveness of the linear feature enhancement method proposed.