SRAGAN: Generating Colour Landscape Photograph from Sketch

Generating sketch from colour landscape photograph is very easy while it is hard to generate colour photograph from landscape sketch. In this paper, a new automatic conversion network, named Sparse Residual Attention Generative Adversarial Networks (SRAGAN), is proposed to generate landscape colour photograph from sketch. Besides of generator adversarial loss, we not only adopt L1-regularized per-pix loss, but also combine L1-regularized perceptual loss together into our model. Due to the sparsity of L1-norm, it can preserve boundary edge information very well, which makes our model can handle well the conversion task of sketch-to-photo. In addition, we proposed a ResAttention block to our network structure, which combines the residual learning blocks with attention module. Experiments show that the landscape colour photographes generated by our SRAGAN looks more natural with bright colour and clear edge information. At the same time, we integrate two models so that we can generate winter-style and summer-style photographes from the same landscape sketch. Experiments demonstrate that our method outperforms many state-of-the-arts both in quantitative and in visual performance.