  Photorealistic style transfer aims to transfer the style of one image to
another, but preserves the original structure and detail outline of the content
image, which makes the content image still look like a real shot after the
style transfer. Although some realistic image styling methods have been
proposed, these methods are vulnerable to lose the details of the content image
and produce some irregular distortion structures. In this paper, we use a
high-resolution network as the image generation network. Compared to other
methods, which reduce the resolution and then restore the high resolution, our
generation network maintains high resolution throughout the process. By
connecting high-resolution subnets to low-resolution subnets in parallel and
repeatedly multi-scale fusion, high-resolution subnets can continuously receive
information from low-resolution subnets. This allows our network to discard
less information contained in the image, so the generated images may have a
more elaborate structure and less distortion, which is crucial to the visual
quality. We conducted extensive experiments and compared the results with
existing methods. The experimental results show that our model is effective and
produces better results than existing methods for photorealistic image
stylization. Our source code with PyTorch framework will be publicly available
at https://github.com/limingcv/Photorealistic-Style-Transfer
