Automatic Image Stylization Using Deep Fully Convolutional Networks

TL;DR

This paper introduces a deep fully convolutional network that automatically applies artistic color and tone stylizations to images by understanding semantic content, enabling diverse and efficient image enhancement.

Contribution

It presents a novel end-to-end deep learning architecture for semantics-aware image stylization that learns local enhancement styles from image pairs.

Findings

Effective stylization demonstrated on existing datasets

Single forward pass achieves real-time performance

Outperforms traditional hand-crafted style methods

Abstract

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.