南京晓庄学院毕业论文

发布时间：2025年09月27日作者:aiycxz.cn

基于深度学习的图像风格迁移学生姓名：刘阳指导教师：王江涛所在学院：信息工程学院专业：计算机科学与技术学号： 18130601提交日期： 2022年5月20日二〇二二年五月# 南京晓庄学院教务处制# 学位论文独创性声明本人郑重声明：所提交的学位论文是本人在导师指导下进行的研究工作和取得的研究成果。本论文中除引文外，所有实验、数据和有关材料均是真实的。本论文中除引文和致谢的内容外，不包含其他人或其它机构已经发表或撰写过的研究成果。其他同志对本研究所做的贡献均已在论文中作了声明并表示了谢意。学位论文作者签名：日期：2022年5月20日# 学位论文使用授权声明研究生在校攻读学位期间论文工作的知识产权单位属南京晓庄学院。学校有权保存本学位论文的电子和纸质文档，可以借阅或上网公布本学位论文的部分或全部内容，可以采用影印、缩印或其他手段复制保存本学位论文。学校可以向国家有关机关或机构送交论文的电子和纸质文档，允许论文被查阅和借阅。（保密论文在解密后遵守此规定）本学位论文属于公开论文。学位论文作者签名：日期：2022年5月20日指导教师签名：日期：2022年5月20日# 基于深度学习的图像风格迁移## 摘要图像风格迁移是计算机视觉领域一个重要的研究方向，主要研究如何将一张图像的风格迁移到另一张图像上，同时保持原图像的内容不变。近年来，随着深度学习的快速发展，基于深度学习的图像风格迁移方法取得了显著进展。本文主要研究基于深度学习的图像风格迁移方法，包括基于图像迭代的优化方法和基于模型迭代的优化方法。首先，本文介绍了图像风格迁移的研究背景和意义，以及国内外研究现状。然后，详细阐述了基于深度学习的图像风格迁移的基本原理，包括卷积神经网络的结构、损失函数的定义以及优化方法的选择。接着，本文重点研究了两种主流的图像风格迁移方法：基于图像迭代的优化方法和基于模型迭代的优化方法。对于基于图像迭代的优化方法，本文以Gatys等人提出的方法为例，详细分析了其实现过程，并通过实验验证了其效果。对于基于模型迭代的优化方法，本文以Johnson等人提出的快速风格迁移方法为例，介绍了其网络结构和训练过程，并通过实验对比了其与基于图像迭代方法的优缺点。最后，本文对基于深度学习的图像风格迁移方法进行了总结，并展望了未来的研究方向。实验结果表明，基于深度学习的图像风格迁移方法能够有效地将艺术风格迁移到内容图像上，生成具有艺术感的图像。其中，基于模型迭代的优化方法在速度上具有明显优势，而基于图像迭代的优化方法在生成图像的质量上更胜一筹。**关键词**: 图像风格迁移；深度学习；卷积神经网络；损失函数；优化方法---# Image Style Transfer Based on Deep Learning## AbstractImage style transfer is an important research direction in the field of computer vision, which mainly studies how to transfer the style of one image to another while preserving the content of the original image. In recent years, with the rapid development of deep learning, image style transfer methods based on deep learning have made significant progress. This paper focuses on image style transfer methods based on deep learning, including optimization methods based on image iteration and optimization methods based on model iteration.First, this paper introduces the research background and significance of image style transfer, as well as the current research status at home and abroad. Then, the basic principles of image style transfer based on deep learning are elaborated, including the structure of convolutional neural networks, the definition of loss functions, and the selection of optimization methods. Next, this paper focuses on two mainstream image style transfer methods: optimization methods based on image iteration and optimization methods based on model iteration. For the optimization method based on image iteration, this paper takes the method proposed by Gatys et al. as an example, analyzes its implementation process in detail, and verifies its effectiveness through experiments. For the optimization method based on model iteration, this paper takes the fast style transfer method proposed by Johnson et al. as an example, introduces its network structure and training process, and compares its advantages and disadvantages with the image iteration-based method through experiments.Finally, this paper summarizes the image style transfer methods based on deep learning and looks forward to future research directions. Experimental results show that image style transfer methods based on deep learning can effectively transfer artistic styles to content images and generate artistic images. Among them, the optimization method based on model iteration has obvious advantages in speed, while the optimization method based on image iteration is superior in the quality of generated images.**Key words:** Image Style Transfer; Deep Learning; Convolutional Neural Network; Loss Function; Optimization Method