Real-Time User-Guided Image Colorization with Learned Deep Priors
This work addresses the challenge of efficient and high-quality image colorization for novice users, offering a real-time tool with significant improvements in quality.
The paper tackles the problem of user-guided image colorization by developing a deep learning system that uses a CNN to map grayscale images and sparse user hints to colorized outputs in real-time, achieving realistic results with just a minute of user input.
We propose a deep learning approach for user-guided image colorization. The system directly maps a grayscale image, along with sparse, local user "hints" to an output colorization with a Convolutional Neural Network (CNN). Rather than using hand-defined rules, the network propagates user edits by fusing low-level cues along with high-level semantic information, learned from large-scale data. We train on a million images, with simulated user inputs. To guide the user towards efficient input selection, the system recommends likely colors based on the input image and current user inputs. The colorization is performed in a single feed-forward pass, enabling real-time use. Even with randomly simulated user inputs, we show that the proposed system helps novice users quickly create realistic colorizations, and offers large improvements in colorization quality with just a minute of use. In addition, we demonstrate that the framework can incorporate other user "hints" to the desired colorization, showing an application to color histogram transfer. Our code and models are available at https://richzhang.github.io/ideepcolor.