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.

本研究介绍了一种新的基于转换器（transformer-based）方法和边缘损失（edge loss）的图像风格转换方法 STT，可以在保持较好的风格特征同时提升内容细节，避免产生过度渲染的模糊结果，并解决常见的内容泄漏问题。实验结果表明，STT 在图像风格转换方面具有与最先进的方法相当的性能。

基于 Transformer 的边缘增强图像风格迁移

Edge Enhanced Image Style Transfer via Transformers

Existing image-to-image translation (I2IT) methods are either constrained to
low-resolution images or long inference time due to their heavy computational
burden on the convolution of high-resolution feature maps. In this paper, we
focus on speeding-up the high-resolution photorealistic I2IT tasks based on
closed-form Laplacian pyramid decomposition and reconstruction. Specifically,
we reveal that the attribute transformations, such as illumination and color
manipulation, relate more to the low-frequency component, while the content
details can be adaptively refined on high-frequency components. We consequently
propose a Laplacian Pyramid Translation Network (LPTN) to simultaneously
perform these two tasks, where we design a lightweight network for translating
the low-frequency component with reduced resolution and a progressive masking
strategy to efficiently refine the high-frequency ones. Our model avoids most
of the heavy computation consumed by processing high-resolution feature maps
and faithfully preserves the image details. Extensive experimental results on
various tasks demonstrate that the proposed method can translate 4K images in
real-time using one normal GPU while achieving comparable transformation
performance against existing methods. Datasets and codes are available:
this https URL

本文提出了一种基于拉普拉斯金字塔分解和重构的高清真实感图像相互转换方法，通过设计适当的低分辨率网络和逐步蒙版策略，可以在保留图像细节的同时，实现高效转换高频细节部分，从而使图像的转换速度和质量均得到显著提高