用VGG19实现可调风格权重的快速艺术图像生成
Dynamic Neural Style Transfer for Artistic Image Generation using VGG19
- 基于VGG19提取特征，分离图像内容与风格
- 支持任意风格权重调节，处理速度更快
- 适合需要灵活控制艺术风格的研究者
人类历史上创造了杰出的艺术作品，但人工智能直到近年才在生成视觉上引人注目的艺术方面取得进展。近年来的突破主要依赖卷积神经网络（CNN）分离并操纵图像的内容与风格，结合纹理合成技术。然而，现有方法仍面临处理时间长、风格图像选择有限、无法调整风格权重比等问题。本文提出一种神经风格迁移系统，可为指定图像添加多种艺术风格，支持风格权重的灵活调整，并显著降低处理时间。该系统采用VGG19模型进行特征提取，确保高质量且保持内容完整性的灵活风格化。
原文摘要 · Abstract (English)
Throughout history, humans have created remarkable works of art, but artificial intelligence has only recently started to make strides in generating visually compelling art. Breakthroughs in the past few years have focused on using convolutional neural networks (CNNs) to separate and manipulate the content and style of images, applying texture synthesis techniques. Nevertheless, a number of current techniques continue to encounter obstacles, including lengthy processing times, restricted choices of style images, and the inability to modify the weight ratio of styles. We proposed a neural style transfer system that can add various artistic styles to a desired image to address these constraints allowing flexible adjustments to style weight ratios and reducing processing time. The system uses the VGG19 model for feature extraction, ensuring high-quality, flexible stylization without compromising content integrity.
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