Abstract:

Mural image restoration is a process of recovering the original image from the damaged mural with missing pixels by using the prior information on the original mural image.In view of the problem that sparse representation does not consider the difference between mural structure information and texture information in mural image restoration,resulting in texture blur and structural line fracture,a group sparse mural restoration algorithm based on multi-scale contour wave decomposition is proposed.First,the nonsubsampled contourlet transform is used to decompose the mural image to be repaired into the low-frequency texture component and high-frequency structure component,which overcomes the deficiency that the difference of mural structure and texture information is not considered in the existing sparse representation of mural repair.Then,the proposed improved group sparse algorithm is used to construct the similar group set of sample blocks for the low-frequency components of texture,and the adaptive group dictionary and sparse coefficients are obtained through the iterative optimization of singular value decomposition and the split Bregman iteration algorithm,so as to complete the repair of low-frequency components.Second,the cubic convolution interpolation algorithm is used to realize the interpolation repair of the high-frequency components of the mural structure.Finally,the restored scale components are fused and reconstructed by the inverse transform of the non down sampled contour wave.Through the restoration experiment of real Dunhuang murals,the results show that the proposed method achieves a better subjective and objective restoration effect and evaluation than the comparison algorithm.

Key words: image reconstruction, mural inpainting, multi-scale decomposition, group sparsity, nonsubsampled contourlet transform