风格迁移技术迅速发展的今天,全局风格迁移技术已基本成型,但它在实际的应用过程中存在不能对图片的目标区域进行局部风格迁移等问题。针对以上问题,本文在卷积神经网络的基础上结合残差网络,提出了一种基于残差式神经网络的局部风格迁移方法。首先,利用掩模技术对内容图进行分割,提取目标区域;其次,卷积神经网络提取图片特征并进行特征融合;然后,使用残差网络加快生成图的形成速度;最后,通过反卷积生成一张只对目标区域完成风格迁移的图片。在Microsoft Coco2014数据集上设计了多个实验,实验结果表明,所提出的基于残差式神经网络的局部风格迁移网络模型具有较好的局部风格转换能力,并且具有较高的执行效率。

With the rapid development of style migration technology, the global style migration technology has basically taken shape, but in the actual application process, there are problems such as the local style migration of the target area of the picture. Aiming at the above problems, this paper combines the residual network based on the convolutional neural network, and proposes a local style migration method based on residual neural network. Firstly, the mask is used to segment the content map to extract the target region. Secondly, the convolutional neural network extracts the image features and performs feature fusion. Then, the residual network is used to speed up the formation of the graph. Finally, the deconvolution is generated. A picture that only completes the style transition for the target area. In this paper, the several experiments are designed on the Microsoft Coco2014 dataset. The experimental results show that the local style migration network model based on residual neural network has better local style conversion ability and higher execution efficiency.