  Recent studies using deep neural networks have shown remarkable success in
style transfer especially for artistic and photo-realistic images. However, the
approaches using global feature correlations fail to capture small, intricate
textures and maintain correct texture scales of the artworks, and the
approaches based on local patches are defective on global effect. In this
paper, we present a novel feature pyramid fusion neural network, dubbed
GLStyleNet, which sufficiently takes into consideration multi-scale and
multi-level pyramid features by best aggregating layers across a VGG network,
and performs style transfer hierarchically with multiple losses of different
scales. Our proposed method retains high-frequency pixel information and low
frequency construct information of images from two aspects: loss function
constraint and feature fusion. Our approach is not only flexible to adjust the
trade-off between content and style, but also controllable between global and
local. Compared to state-of-the-art methods, our method can transfer not just
large-scale, obvious style cues but also subtle, exquisite ones, and
dramatically improves the quality of style transfer. We demonstrate the
effectiveness of our approach on portrait style transfer, artistic style
transfer, photo-realistic style transfer and Chinese ancient painting style
transfer tasks. Experimental results indicate that our unified approach
improves image style transfer quality over previous state-of-the-art methods,
while also accelerating the whole process in a certain extent. Our code is
available at https://github.com/EndyWon/GLStyleNet.
