In the process of Dunhuang mural restoration, dictionary initialized random selection falls into local optimum easily and only the color Euclidean distance is used as the standard for image block grouping, which leads to the problems such as structure blur and line discontinuity after image restoration. An algorithm for Dunhuang mural inpainting based on Gabor transform and group sparse representation is proposed in this paper. First, the similar structure group is established using mutual information as the criterion of image block grouping, which makes group sparse representation more reasonable. Second, the Gabor wavelet transform is used to extract the feature information of similar structure groups, and the feature dictionary of the structure group is initialized by means of PCA dimension reduction, which can avoid the disadvantage of dictionary initialized random selection. Finally, the SVD decomposition and the split Bregman iteration method are used to learn the structure group dictionary and the sparse coefficients to complete the mural image restoration. The experimental results show that, compared with the other algorithms, the algorithm proposed in this paper has achieved good subjective and objective restoration effects.