摘 要
近年来，基于深度学习的图像风格迁移技术取得了显著进展，广泛应用于艺术创作、视觉设计和图像处理等领域。然而，现有方法在保持内容结构一致性、提升风格迁移质量及降低计算复杂度方面仍存在诸多挑战。为此，本研究旨在提出一种优化的深度学习图像风格迁移框架，以解决上述问题。本文首先分析了主流风格迁移模型的优缺点，进而引入多尺度特征融合机制与自适应权重调整策略，以增强生成图像的内容保真度与风格表达能力。此外，设计了一种轻量化网络结构，在保证迁移效果的同时显著减少模型参数与推理时间。实验结果表明，所提方法在多个公开数据集上均取得优于现有算法的表现，无论是在视觉质量还是定量指标上均有明显提升。本研究的主要贡献在于构建了一个兼顾性能与效率的风格迁移系统，并为后续相关研究提供了新的思路和技术支持。
关键词：深度学习；图像风格迁移；多尺度特征融合；自适应权重调整；轻量化网络结构
Research on Optimization of Image Style Transfer Based on Deep Learning
Abstract: In recent years, deep learning-based image style transfer techniques have achieved significant progress and have been widely applied in fields such as art creation, visual design, and image processing. However, existing methods still face numerous challenges in maintaining content structure consistency, improving style transfer quality, and reducing computational complexity. To address these issues, this study aims to propose an optimized deep learning fr amework for image style transfer. This paper first analyzes the strengths and weaknesses of mainstream style transfer models, and then introduces a multi-scale feature fusion mechanism along with an adaptive weight adjustment strategy to enhance both the content fidelity and style ex pression capability of the generated images. Furthermore, a lightweight network architecture is designed to significantly reduce model parameters and inference time while maintaining high-quality transfer results. Experimental results demonstrate that the proposed method outperforms existing algorithms on multiple public datasets, achieving notable improvements not only in visual quality but also in quantitative metrics. The main contribution of this research lies in the development of a style transfer system that balances performance and efficiency, offering new insights and technical support for future studies in this area.
Keywords: Deep Learning; Image Style Transfer; Multi-Scale Feature Fusion; Adaptive Weight Adjustment; Lightweight Network Architecture
目 录
1绪论 1
1.1研究背景和意义 1
1.2研究现状 1
1.3研究方法 1
2图像风格迁移的基本原理与技术演进 2
2.1深度学习在图像风格迁移中的应用基础 2
2.2典型图像风格迁移模型的发展历程 2
2.3当前主流方法的优势与局限性分析 3
2.4风格迁移技术在实际场景中的典型应用 3
3图像风格迁移中的关键问题与优化方向 4
3.1内容与风格特征的分离与融合机制 4
3.2多尺度与多风格迁移的实现策略 4
3.3迁移过程中的细节保留与失真控制 5
3.4实时性与计算效率的优化路径 5
4基于深度学习的图像风格迁移优化方法设计 6
4.1网络结构改进与特征提取优化 6
4.2损失函数的设计与权重调节策略 6
4.3数据集构建与增强对迁移效果的影响 7
4.4实验验证与性能评估指标设定 7
结论 7
参考文献 9
致 谢 10
