Portrait Sketch Synthesis via Mish-Gated U-net and GANs
Jian Chen, Gang Liu, Yuan Gui, Xinyun Wu · 2022
GANs structure. However, those methods based on GANs did not get a better effect, their results usually have obvious quality problems such as serious distortion and visual artifacts. In this paper, we propose a novel portrait sketch synthesis approach based on the mish-gated U-net (MGUnet) and GANs to address the above problems. The MGUnet is an improvement on the classical U-net, which utilizes mish activation function to build a new gating module, and embeds the module in U-net to enhance the synthesis performance of the neural network. The proposed method can effectively eliminate artifacts and enhance the effect of synthesized images. The results of our experiments demonstrate that our method can convert satisfactory portrait sketches for portrait sketch synthesis.