基于深度学习的图片风格转换系统
摘 要
随着深度学习技术的发展,图像风格迁移技术在艺术创作和图像编辑领域得到了广泛的应用。本文旨在介绍一种基于深度学习的图像风格迁移系统,该系统能够将特定的艺术风格应用到目标图像上,实现内容与风格的完美融合。系统采用卷积神经网络(CNN)作为特征提取器,特别是利用预训练的VGG网络来捕捉内容和风格的深层特征。通过最小化内容图片与目标风格图片之间的特征差异,并保持生成图像与原始内容图片的高相似度,系统能够生成具有艺术风格的新图像。此外,本文还提供了完整的Python实现代码,以及使用PyQT构建的用户界面(UI),使得非专业人士也能轻松地进行图像风格迁移操作。系统支持多种风格的迁移模型,用户可以根据自己的喜好选择不同的风格进行转换。本文的研究成果不仅推动了图像风格迁移技术的发展,也为相关领域的研究者和爱好者提供了一个实用的工具和平台。
关键词:图像风格迁移;深度学习;卷积神经网络;VGG网络;PyQT
Image style conversion system based on deep learning
Abstract
With the development of deep learning technology, image style transfer technology has been widely applied in the field of art creation and image editing. This paper aims to introduce an image style transfer system based on deep learning, which can apply a specific art style to the target image and realize the perfect fusion of content and style. The system uses the convolutional neural network (CNN) as the feature extractor, especially using the pre-trained VGG network to capture the deep characteristics of the content and style. By minimizing the feature differences between the content image and the target style image, and maintaining the high similarity between the generated image and the original content image, the system is able to generate new images with artistic style. In addition, this paper provides a complete implementation code for Python and a user interface (UI) built using PyQT, making it easy for non-professionals to perform image style migration operations. The system supports multiple styles of migration models, and users can choose different styles according to their own preferences. The research results of this paper not only promote the development of image style migration technology, but also provide a practical tool and platform for researchers and enthusiasts in related fields.
Key words: image style migration; deep learning; convolutional neural network; VGG network; PyQT
目 录
摘 要
Abstract
1绪论
1.1 研究背景
1.2 研究目的和意义
1.2.1 研究的应用价值
1.2.2 研究的前沿性和学术性
1.3 研究内容
1.3.1 风格建模
1.3.2 图像重建
2 基于CNN的图像风格迁移算法
2.1 CNN-IST研究框架
2.2 CNN-IST网络基本结构
2.3 PSPM 网络框架
2.4 PSPM算法理论
2.5 实验设计与算法评估
2.5.1 实验细节和参数设置
2.5.2 定性评估
2.5.3 定量评估
3 图片迁移原理及模型设计
3.1 基本原理
3.2 训练模型
3.3 模型使用方法
3.4 风格迁移示例
4 系统实现
4.1 系统主界面
4.2 图片风格转换界面
4.3 效果演示
4.3.1 图片风格转移
4.3.2 更改图片风格
5 总结与展望
5.1 研究总结
5.2 研究展望
参考文献
致 谢
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