关键词: 深度学习, 多尺度 CycleGAN, 卷积神经网络, 特征增强模块, 像素注意力模块

Abstract: At present, Face sketch synthesis has a series of problems, such as generateing fuzzy outline, lacking of detail texture and so on. Therefore, using CycleGAN(Cycle-Generative Adversarial Networks) as a solution to build multi-scale cyclegan is proposed. Method innovation is mainly reflected in: The generator adopts the deep supervised U-net++ structure as the basis, and performs down sampling dense jump connection at its decoder; The encoder end of the generator designs the channel attention and spatial attention mechanism to form a feature enhancement module; a pixel attention module is added to the generator. Compared with some existing classical algorithms, from the subjective visual evaluation and using the existing four image quality evaluation algorithms for quality evaluation, the experimental results show that this algorithm can better synthesize the geometric edge and facial detail information of sketch image, and improve the quality of sketch image.

Key words: deep learning, multi-scale cycle-generative adversarial networks (CycleGAN), convolutional neural networks(CNN), feature enhancement module, pixel attention module