Back in time:digital restoration techniques for the millennium Dunhuang murals

In the long history of more than 1 500 years,Dunhuang murals suffered from various deteriorations causing irreversible damage such as falling off,fading,and so on.However,the existing Dunhuang mural restoration methods are time-consuming and not feasible to facilitate cultural dissemination and permanent inheritance.Inspired by cultural computing using artificial intelligence,gated-convolution-based dehaze net(GD-Net) was proposed for Dunhuang mural refurbishment and comprehensive protection.First,a neural network with gated convolution was applied to restore the falling off areas of the mural to ensure the integrity of the mural content.Second,a dehaze network was applied to enhance image quality to cope with the fading of the mural.Besides,a Dunhuang mural dataset was presented to meet the needs of deep learning approach,containing 1 180 images from the Cave 290 and Cave 112 of the Mogao Grottoes.The experimental results demonstrate the effectiveness and superiority of GD-Net.