Visual‐attention GAN for interior sketch colourisation

Abstract In the professional field of interior designing, sketch colouring is often a time‐consuming and vapidity task. The traditional neural network does not handle the semantic relationship of sketch lines well, and the colouring effect is unsatisfactory. This paper proposes visual‐attention generative adversarial network (VAGAN), which enhances the processing effect of edge semantics, strengthens the network to line edge recognition ability, as well as reduces colour overflow and improved model colouring result. In addition, a two‐stage training mode is used to simplify the training of rare samples. The simple line draft input into the trained VAGAN, output natural, realistic colour pictures. The experimental results show that, compared with the existing methods, the proposed method can better deal with the problem of sketch and generate stable and reliable images