深度生成模型在图像合成中的应用研究
摘 要
随着信息技术的迅猛发展，图像合成技术在众多领域展现出巨大应用潜力，深度生成模型凭借其强大的非线性表达能力成为图像合成研究的热点。本研究旨在探索深度生成模型在图像合成中的应用，以期为相关领域提供新的思路与方法。基于此，选取了生成对抗网络（GAN）、变分自编码器（VAE）等典型深度生成模型，通过构建改进模型结构、优化损失函数等方式提升模型性能。实验采用公开数据集进行训练与测试，在人脸图像、风景图像等多种类型图像合成任务中取得了良好效果。相较于传统方法，所提方案能够生成更高质量、更多样化的图像，尤其在细节纹理生成方面表现出色。创新点在于融合多尺度特征信息增强模型对图像细节的刻画能力，并引入注意力机制使模型聚焦于关键区域，提高合成图像的真实感。
关键词：深度生成模型 图像合成 生成对抗网络
Abstract
With the rapid development of information technology, the image synthesis technology has shown great application potential in many fields, and the deep generation model has become the hotspot of the image synthesis research with its powerful nonlinear ex pression ability. This study aims to explore the application of deep generation model in image synthesis, in order to provide new ideas and methods for related fields. Based on this, the typical deep generation models such as generative adversarial network (GAN) and variational autoencoder (VAE) are selected to improve the model performance by building and improving the model structure and optimizing the loss function. The experiment used publicly available data sets for training and testing, and achieved good results in various types of image synthesis tasks, such as face image and landscape image. Compared with the traditional method, the proposed scheme can generate higher quality and more diverse images, especially in the generation of detailed texture excellent performance. The innovation point is the ability of integrating multi-scale feature information to enhance the model to depict the image details, and introduce the attention mechanism to make the model focus on the key areas, and improve the reality of the synthetic image.
Keyword:Deep Generative Model Image Synthesis Generative Adversarial Network
目 录
1绪论 1
1.1深度生成模型研究背景与意义 1
1.2图像合成领域研究现状综述 1
1.3本文研究方法概述 2
2深度生成模型理论基础 2
2.1生成对抗网络原理分析 2
2.2变分自编码器技术探讨 3
2.3扩散模型机制解析 3
3图像合成关键技术研究 4
3.1数据集构建与预处理 4
3.2模型训练优化策略 4
3.3合成图像质量评估 5
4应用场景与案例分析 6
4.1艺术创作中的应用 6
4.2医疗影像合成探索 6
4.3虚拟现实领域实践 7
结论 7
参考文献 9
致谢 10
摘 要
随着信息技术的迅猛发展，图像合成技术在众多领域展现出巨大应用潜力，深度生成模型凭借其强大的非线性表达能力成为图像合成研究的热点。本研究旨在探索深度生成模型在图像合成中的应用，以期为相关领域提供新的思路与方法。基于此，选取了生成对抗网络（GAN）、变分自编码器（VAE）等典型深度生成模型，通过构建改进模型结构、优化损失函数等方式提升模型性能。实验采用公开数据集进行训练与测试，在人脸图像、风景图像等多种类型图像合成任务中取得了良好效果。相较于传统方法，所提方案能够生成更高质量、更多样化的图像，尤其在细节纹理生成方面表现出色。创新点在于融合多尺度特征信息增强模型对图像细节的刻画能力，并引入注意力机制使模型聚焦于关键区域，提高合成图像的真实感。
关键词：深度生成模型 图像合成 生成对抗网络
Abstract
With the rapid development of information technology, the image synthesis technology has shown great application potential in many fields, and the deep generation model has become the hotspot of the image synthesis research with its powerful nonlinear ex pression ability. This study aims to explore the application of deep generation model in image synthesis, in order to provide new ideas and methods for related fields. Based on this, the typical deep generation models such as generative adversarial network (GAN) and variational autoencoder (VAE) are selected to improve the model performance by building and improving the model structure and optimizing the loss function. The experiment used publicly available data sets for training and testing, and achieved good results in various types of image synthesis tasks, such as face image and landscape image. Compared with the traditional method, the proposed scheme can generate higher quality and more diverse images, especially in the generation of detailed texture excellent performance. The innovation point is the ability of integrating multi-scale feature information to enhance the model to depict the image details, and introduce the attention mechanism to make the model focus on the key areas, and improve the reality of the synthetic image.
Keyword:Deep Generative Model Image Synthesis Generative Adversarial Network
目 录
1绪论 1
1.1深度生成模型研究背景与意义 1
1.2图像合成领域研究现状综述 1
1.3本文研究方法概述 2
2深度生成模型理论基础 2
2.1生成对抗网络原理分析 2
2.2变分自编码器技术探讨 3
2.3扩散模型机制解析 3
3图像合成关键技术研究 4
3.1数据集构建与预处理 4
3.2模型训练优化策略 4
3.3合成图像质量评估 5
4应用场景与案例分析 6
4.1艺术创作中的应用 6
4.2医疗影像合成探索 6
4.3虚拟现实领域实践 7
结论 7
参考文献 9
致谢 10
