SeparaFill: Two generators connected mural image restoration based on generative adversarial network with skip connect

Abstract Mural is an important component of culture and art of Dunhuang in China. Unfortunately, these murals had been ruined or are being ruined by some diseases such as cracking, hollowing, falling off, mildew, dirt, and so on. Existing image restoration algorithms have problems such as incomplete repair and disharmonious texture during large-area repair, so the effect of mural image disease area repair is poor. Due to lack of a standard mural datasets, Dunhuang mural datasets are created in the paper. Meanwhile, our network architecture SeparaFill is proposed which connects two generators based on U-Net. Based on the characteristics of the painting, the contour line pixel area of the mural image is innovatively separated from the content pixel area. Firstly, the contour restoration generator network with skip connect and hierarchical residual blocks is employed to repair contour lines. Then, the color mural image is repaired by the content completion network with guide of the repaired contour. Full resolution branches and generator branches of the U type are exploited in content completion generators. Convolution layers of different kernel sizes are fused to improve the reusability of the underlying features. Finally, global and local discriminant networks are applied to determine whether the repaired mural image is authentic in terms of both the modified and unmodified areas. The proposed SeparaFill shows good performance in restoring the line structure of the damaged mural images and retaining the contour information of the mural images. Compared with existing restoration algorithms in mural real damage repair experiment, our algorithm increases the peak signal-to-noise ratio (PSNR) by an average of 1.1–4.3 dB and the structural similarity (SSIM) values were slightly improved. Experimental results reveal the good performance of the proposed model, which can contribute to digital restorations of ancient murals.