July 5, 2025

Neural Style Transfer for Image Stylization

Key Points

The proposed framework balances content preservation and style transformation effectively, enhancing image quality and visual appeal.
Implementation of the neural style transfer network yielded significant results on the WikiArt and COCO datasets, showcasing transformative capabilities.
Deep learning techniques facilitate extraction of semantic features, overcoming challenges in traditional image stylization approaches.
Achieving nuanced style shifts while maintaining content integrity presents important implications for future computer vision applications.

Abstract

Image style migration, exploring the transformation of visual styles from one image to another, has become a focal point in computer vision research. The semantic and stylistic features of images are difficult to express directly through mathematical models, which greatly increases the difficulty of image stylization. Fortunately, approaches based on deep learning have shown promise in extracting deep semantic information from images, facilitating notable advancements in image style transfer. However, achieving a balance between content preservation and style transformation remains a formidable challenge. This paper introduces a neural style transfer network (NSTN) that aims to maintain image semantics while performing style transfer effectively. The NSTN framework comprises a process block, a style block, and an ascension decoder, working in concert to achieve nuanced style shifts while preserving the content integrity. Implementation results on the WikiArt and COCO datasets demonstrate the model's effectiveness in achieving a harmonious balance between content preservation and style integration.

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