Abstract: The use of virtual technology is important for the restoration of relics,which are often damaged by improper preservation or physical restoration methods.Existing traditional image restoration techniques and deep learning-based restoration methods are mainly suitable for images with simple structural textures,small damaged areas,or natural images with regular damage,and cannot be directly applied to heritage images.Using landscape painting image restoration as an example,a two-stage method for restoration of heritage images based on a multi-scale attention mechanism is proposed in this paper to address the problems of complex structural textures,discreet colouring and small size of existing datasets of heritage images.The method firstly performs coarse restoration of the overall structure and base tones of the image based on the global attention mechanism,then performs local fine restoration of small structures and fine textures of the image using the local attention mechanism and the residual module,as well as global fine restoration of large structures and textures using the contextual attention mechanism on the result of coarse restoration to borrow information accurately at a distance.Finally,the local and global fine restoration results are fused to achieve the restoration of heritage images.The proposed method has the advantage of improving the peak signal-to-noise ratio by 3.76 dB and the structural similarity by 0.034 compared with the comparative methods on average.Both the subjective and objective analysis of the experimental results show that the method has some advantages in semantic rationality,information accuracy and visual naturalness compared with the existing methods,and has a high potential for application in the field of heritage restoration.

Key words: Heritage image, Image restoration, Deep learning, Two stage model, Muti-scale attention mechanism