Abstract：When the existing automatic portrait coloring algorithms are directly applied to scene sketches, distortion phenomena are caused, such as wrong colorization and checkerboard artifacts,due to the diversified line semantics of scene sketches. To address this issue, an automatic colorization algorithm with anime effect for scene sketches is put forward. The structure of U-Net generator in the existing automatic portrait coloring algorithms is improved and enhanced based on the conditional generative adversarial network. A double-layer information extraction U-Net(DIEU-Net) is designed for automatic anime effect colorization of scene sketches. Firstly, the double-convolution sub-module prominence-information extraction of a scene sketch(IESS) is designed. Then, a module integrating double-layer IESS and residual structure is inserted into different stages of the proposed generator. Thus, the global learning ability of the generator on important features, like colors and positions related to the sketch, are enhanced, and the network degradation problems caused by vanishing gradients as the network deepens, are alleviated. Moreover, the deconvolution in U-Net is replaced by the operations of convolution and upsample to suppress the occurrence of the checkerboard artifacts. Experimental results show that the proposed algorithm performs well in avoiding the distortion phenomenon and achieves more reasonable and natural coloring effect than other algorithms. Furthermore, the proposed algorithm can be applied to automatic anime coloring of various types of scene sketches.
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2020, Vol. 33 
