This article compares two style transfer methods in image processing: the traditional method, which synthesizes new images by stitching together small patches from existing images, and a modern machine learning-based approach that uses a segmentation network to isolate foreground objects and apply style transfer solely to the background. The traditional method excels in creating artistic abstractions but can struggle with seamlessness, whereas the machine learning method preserves the integrity of foreground elements while enhancing the background, offering improved aesthetic quality and computational efficiency. Our study indicates that machine learning-based methods are more suited for real-world applications where detail preservation in foreground elements is essential.

翻译：本文比较了图像处理中的两种风格迁移方法：传统方法通过拼接现有图像的小块来合成新图像，以及一种基于现代机器学习的方法，该方法使用分割网络隔离前景对象并仅对背景应用风格迁移。传统方法在创造艺术抽象方面表现出色，但可能在无缝性方面存在困难；而机器学习方法在增强背景的同时保持了前景元素的完整性，提供了更高的美学质量和计算效率。我们的研究表明，基于机器学习的方法更适用于现实世界应用，其中前景元素的细节保留至关重要。