Abstract
Throughout history, humans have created remark- able 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.
Abstract (translated)
历史上，人类创造了令人惊叹的艺术作品，而人工智能在生成视觉吸引人的艺术方面仅最近才开始取得进展。近年来的突破主要集中在使用卷积神经网络（CNN）来分离和操作图像的内容与风格，并应用纹理合成技术。然而，目前许多技术仍然面临一些障碍，包括处理时间长、可供选择的风格图片有限以及无法调整风格权重比等问题。为了解决这些问题，我们提出了一种基于VGG19模型进行特征提取的神经样式迁移系统。该系统能够向目标图像添加各种艺术风格，并允许灵活地调整风格权重比例以减少处理时间，同时确保在不损害内容完整性的情况下实现高质量和灵活性的风格化。
URL
https://arxiv.org/abs/2501.09420
