In repairing murals based on sparse representation， the dictionary construction is single， and the restoration of details is inadequate. Therefore， an improved sparse mural restoration algorithm using joint adaptive learning of multiple dictionaries is proposed. First， a non-subsampled shearlet transform （NSST） is used to perform multi-scale decomposition on the mural image to obtain low-frequency structural components and high-frequency texture components， overcoming the problem of neglecting the differences in texture and structure information in mural restoration using sparse representation. Then， a sparse method of multiple dictionary adaptive learning is proposed. The low-frequency texture image is clustered based on the similarity of features between pixels to construct multiple sparse sub-dictionaries， and the low-frequency component restoration is completed through singular value decomposition and split Bregman iteration optimization. Then， the pulse-coupled neural network mechanism is introduced to restore the high-frequency structural sub-band image of the mural image. Finally， the NSST inverse transform is used to merge and complete the restoration. Experimental results on actual murals show that the proposed algorithm effectively preserves significant information， such as the structure and texture layers of the mural image， and achieves better visual effects and objective evaluations than the compared algorithms.