基于Gram矩阵和卷积神经网络的风格迁移算法

Style Transfer Algorithm Based on Gram Matrix and Convolutional Neural Network

如何获得更好的图像风格迁移效果一直是图像处理领域中经典问题之一。针对传统方法存在表现欠佳并具有较强局限性的问题,讨论了卷积神经网络(convolutional neural network,CNN)不同层提取图像特征的特点,提出了一种基于Gram矩阵和CNN的图像风格迁移算法,并设计了多种艺术风格的迁移实验,最后通过实验验证所提出的算法可比传统算法在更短的时间更好地实现艺术图像的风格迁移,说明该算法在图像特征提取及图像风格迁移任务上更具优势。

How to get a better effect for the image style transfer is one of the typical problems in the field of image processing.Aiming at the problems of poor performance and strong limitation in the traditional methods,this paper firstly discussed on the characteristics of image features extraction in the different layers of CNN(convolutional neural networks),then an image style transfer algorithm based on Gram matrix and CNN was proposed,and a variety of art style transfer experiments were designed.The experimental results showed that the proposed algorithm could better implement the style transfer of art images in a shorter time than traditional algorithms,and the experiments verified the advantages of the proposed algorithm in image feature extraction and style transfer tasks.