Edge Enhanced Image Style Transfer via Transformers

TL;DR

This paper introduces STT, a transformer-based image style transfer method that enhances content details with an edge loss, achieving high-quality stylization while reducing content leakage.

Contribution

The paper proposes a novel transformer-based approach with an edge loss to improve style transfer quality and preserve content details.

Findings

STT achieves comparable performance to state-of-the-art methods.

The edge loss effectively enhances content details.

The method alleviates content leak issues.

Abstract

In recent years, arbitrary image style transfer has attracted more and more attention. Given a pair of content and style images, a stylized one is hoped that retains the content from the former while catching style patterns from the latter. However, it is difficult to simultaneously keep well the trade-off between the content details and the style features. To stylize the image with sufficient style patterns, the content details may be damaged and sometimes the objects of images can not be distinguished clearly. For this reason, we present a new transformer-based method named STT for image style transfer and an edge loss which can enhance the content details apparently to avoid generating blurred results for excessive rendering on style features. Qualitative and quantitative experiments demonstrate that STT achieves comparable performance to state-of-the-art image style transfer methods while alleviating the content leak problem.