Neural Network Based Image Style Transfer: A Survey

WANG Wei; ZHANG Jing-yi; WEN Yu-hui; WEI Yun-chao

doi:10.12263/DZXB.20240930

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Neural Network Based Image Style Transfer: A Survey

SURVEYS AND REVIEWS | 更新时间：2025-08-18

- Neural Network Based Image Style Transfer: A Survey
- ACTA ELECTRONICA SINICA Vol. 53, Issue 5, Pages: 1692-1712(2025)
- 作者机构：
  
  北京交通大学计算科学与技术学院，北京 100044
- 作者简介：
- 基金信息：
  
  Fundamental Research Funds for the Central Universities(2022XKRC015);National Natural Science Foundation of China(62372033)
- DOI：10.12263/DZXB.20240930
  CLC： TP183;
- Received：16 October 2024，
  
  Revised：2025-04-21，
  
  Published：25 May 2025
- 稿件说明：
移动端阅览
王伟, 张静宜, 温玉辉, 等. 基于神经网络的图像风格迁移算法综述[J]. 电子学报, 2025, 53(05): 1692-1712.

WANG Wei, ZHANG Jing-yi, WEN Yu-hui, et al. Neural Network Based Image Style Transfer: A Survey[J]. Acta Electronica Sinica, 2025, 53(05): 1692-1712.
王伟, 张静宜, 温玉辉, 等. 基于神经网络的图像风格迁移算法综述[J]. 电子学报, 2025, 53(05): 1692-1712. DOI：10.12263/DZXB.20240930

WANG Wei, ZHANG Jing-yi, WEN Yu-hui, et al. Neural Network Based Image Style Transfer: A Survey[J]. Acta Electronica Sinica, 2025, 53(05): 1692-1712. DOI：10.12263/DZXB.20240930

摘要

风格迁移作为图像编辑领域的一个关键研究方向，在艺术创作等领域展现出广泛的应用前景.自Gatys等人提出使用深度卷积特征间相关性捕获纹理信息并基于此实现风格迁移后，大量基于神经网络的风格迁移算法不断涌现.近年来随着各式生成模型的兴起，将生成对抗网络、扩散模型等生成模型引入风格迁移工作获得了新的关注.此外，图像-文本跨模态任务的突破使得文本引导条件下的图像风格迁移成为可能.本文对当前先进的研究方法进行分类和描述.具体地，依据引导条件差异，将现有方法划分为图像引导的图像风格迁移方法、文本引导的图像风格迁移方法；依据网络架构的不同，将现有方法细分为基于自编码器的方法、基于生成对抗网络的方法、基于扩散模型的方法以及基于其他模型架构的方法，对当前图像风格迁移技术的研究进行全面的综述与分析.随后，介绍了图像风格迁移任务的数据集和评价体系，并从定量与定性方面对部分最先进的图像风格迁移方法进行实验和比较.最后，讨论了当前图像风格迁移技术面临的挑战，并对未来研究方向提出了展望.

Abstract

As a key research direction in the field of image editing

style transfer has shown a broad applications in artistic creation and related fields. Since Gatys et al. proposed the use of deep convolutional inter-feature correlations to capture texture information for style transfer

numerous neural style transfer algorithms have emerged. Recently

with the rise of various generative models

particularly the introduction of generative adversarial networks and diffusion models

style transfer work has gained new attention. Additionally

breakthroughs in image-text cross-modal tasks have made text-guided image style transfer possible. This paper presents a comprehensive review of the latest developments in style transfer techniques

classifying methods into image-guided and text-guided categories based on the guiding conditions. Furthermore

the methods are categorized into autoencoder-based approaches

GAN-based methods

diffusion model-based methods

and other architectural variants. This paper also introduces relevant dataset and evaluation metrics for image style transfer tasks

and compares state-of-the-art methods in terms of quantitative and qualitative aspects. Finally

the paper discusses the challenges and g provides insights into potential future research directions.

关键词

Keywords

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