  Color and tone stylization strives to enhance unique themes with artistic
color and tone adjustments. It has a broad range of applications from
professional image postprocessing to photo sharing over social networks.
Mainstream photo enhancement softwares provide users with predefined styles,
which are often hand-crafted through a trial-and-error process. Such photo
adjustment tools lack a semantic understanding of image contents and the
resulting global color transform limits the range of artistic styles it can
represent. On the other hand, stylistic enhancement needs to apply distinct
adjustments to various semantic regions. Such an ability enables a broader
range of visual styles. In this paper, we propose a novel deep learning
architecture for automatic image stylization, which learns local enhancement
styles from image pairs. Our deep learning architecture is an end-to-end deep
fully convolutional network performing semantics-aware feature extraction as
well as automatic image adjustment prediction. Image stylization can be
efficiently accomplished with a single forward pass through our deep network.
Experiments on existing datasets for image stylization demonstrate the
effectiveness of our deep learning architecture.
