A convolutional neural artistic stylization algorithm for suppressing image distortion  

一种抑制图像扭曲的卷积神经艺术风格化算法

作　　者：SHEN Yu YANG Qian ZHANG Hongguo WANG Lin 沈瑜;杨倩;张泓国;王霖(兰州交通大学电子与信息工程学院,甘肃兰州730070)

机构地区：[1]School of Electronics and Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China

出　　处：《Journal of Measurement Science and Instrumentation》2021年第3期287-294,共8页测试科学与仪器（英文版）

基　　金：National Natural Science Foundation of China(No.61861025)。

摘　　要：Aiming at the problems of image semantic content distortion and blurred foreground and background boundaries during the transfer process of convolutional neural image stylization,we propose a convolutional neural artistic stylization algorithm for suppressing image distortion.Firstly,the VGG-19 network model is used to extract the feature map from the input content image and style image and to reconstruct the content and style.Then the transfer of the input content image and style image to the output image is constrained in the local affine transformation of the color space.And the Laplacian matting matrix is constructed by combining the local affine of the input image RGB channel.For each output blocks,affine transformation maps the RGB value of the input image to the corresponding output and position,which realizes the constraint of semantic content and the control of spatial layout.Finally,the synthesized image is superimposed on the white noise image and updated iteratively with the back propagation algorithm to minimize the loss function to complete the image stylization.Experimental results show that the method can generate images with obvious foreground and background edges,clear texture,restrained semantic content-distortion,realized spatial constraint and color mapping of the transfer images,and made the stylized images visually satisfactory.

关 键 词：neural network style transfer deep learning affine transformation 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]