. Our proposed method colorizes a grayscale image (le ), guided by sparse user inputs (second), in real-time, providing the capability for quickly generating multiple plausible colorizations (middle to right). Photograph of Migrant Mother by Dorothea Lange, 1936 (Public Domain). We propose a deep learning approach for user-guided image colorization. e 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-de ned 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 e cient input selection, the system recommends likely colors based on the input image and current user inputs. e colorization is performed in a single feed-forward pass, enabling realtime use. Even with randomly simulated user inputs, we show that the proposed system helps novice users quickly create realistic colorizations, and o ers 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.
Reference REF uses a CNN to directly map a grayscale image, along with the sparse, local user hints or the global histogram of the LAB and HSV information, to produce a colored output.
Real-time user-guided image colorization with learned deep priors
TOGS
Computer Science