基于亮度与色度信息的深度学习图像风格迁移算法研究  

Deep Learning Algorithm for Image Style Transfer Based on Luminance and Color Channels

作　　者：杨慧炯 韩燕丽 郭芸俊 YANG Huijiong;HAN Yanli;GUO Yunjun(Department of Computer Engineering,Taiyuan Institute of Technology,Taiyuan 030008,China)

机构地区：[1]太原工业学院计算机工程系

出　　处：《重庆理工大学学报（自然科学）》2019年第7期145-151,159,共8页Journal of Chongqing University of Technology：Natural Science

基　　金：山西省科技攻关项目(20140313025-2);山西省高等学校科技开发项目(20121119)

摘　　要：深度学习技术为图像风格迁移技术的突破提供了可能,无论在日常生活还是在学术、工业应用中都有很高的价值。目前已有的深度学习图像迁移算法虽然取得了突破性进展,但是在纹理细节、笔触形状等艺术风格以及大面积色块区域变化特征的提取中往往无法达到满意的效果。为此,通过综合考虑图像的颜色和亮度信息构建一个多尺度分层网络,在提取图像整体艺术风格的同时对纹理细节和大面积色块区域的微小亮度变化特征进行细化。相对于已有的深度学习图像风格迁移算法,在不增加软硬件和时间成本的前提下,可达到更为满意的迁移效果。Deep learning technology provides the possibility for the breakthrough of image style transfer technology,which is of great value both in daily life and in academic and industrial applications.Although the existing deep learning algorithms for image style transfer have made breakthrough progress,these algorithms often fail to achieve satisfactory results in the art styles such as texture details,stroke shapes and in the extraction of variable features on large color blocks.A multi-scale hierarchical network is constructed by considering both the color and Luminance information of the image and it can refine the texture details and the tiny variable features of brightness on the large color blocks while extracting the overall artistic style of the image.Compared with the existing deep learning algorithms for image style transfer,the algorithm in the paper can achieve a more satisfactory effect of image style transfer without increasing the costs of hardware and software and time.

关 键 词：图像风格迁移 深度学习 深度卷积网络 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] TP181[自动化与计算机技术—计算机科学与技术]