  The basic principle of the patch-matching based style transfer is to
substitute the patches of the content image feature maps by the closest patches
from the style image feature maps. Since the finite features harvested from one
single aesthetic style image are inadequate to represent the rich textures of
the content natural image, existing techniques treat the full-channel style
feature patches as simple signal tensors and create new style feature patches
via signal-level fusion, which ignore the implicit diversities existed in style
features and thus fail for generating better stylised results. In this paper,
we propose a Retinex theory guided, channel-grouping based patch swap technique
to solve the above challenges. Channel-grouping strategy groups the style
feature maps into surface and texture channels, which prevents the
winner-takes-all problem. Retinex theory based decomposition controls a more
stable channel code rate generation. In addition, we provide complementary
fusion and multi-scale generation strategy to prevent unexpected black area and
over-stylised results respectively. Experimental results demonstrate that the
proposed method outperforms the existing techniques in providing more
style-consistent textures while keeping the content fidelity.
