Style Transfer: From Stitching to Neural Networks
This work addresses style transfer in image processing for applications requiring detail preservation, but it is incremental as it compares existing methods without introducing new techniques.
The paper compared traditional patch-stitching and modern neural network-based style transfer methods, finding that the neural approach better preserves foreground details and offers improved aesthetic quality and computational efficiency.
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.