text: The colorization of grayscale images is an ill-posed problem, with multiple correct solutions. In this paper, an adversarial learning approach is proposed. A generator network is used to infer the chromaticity of a given grayscale image. The same network also performs a semantic classification of the image. This network is framed in an adversarial model that learns to colorize by incorporating perceptual and semantic understanding of color and class distributions. The model is trained via a fully self-supervised strategy. Qualitative and quantitative results show the capacity of the proposed method to colorize images in a realistic way, achieving top-tier performances relative to the state-of-the-art.
  @cite_32: 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 this https URL
  @cite_33: This paper proposes the first deep learning approach for exemplar-based colorization, in which a convolutional neural network robustly maps a grayscale image to a colorized output given a color reference.
summary: Other CNN based approaches are combined with user interactions. For instance, Zhang al @cite_32 propose to train a deep network given the grayscale version and a set of sparse user inputs. This allows the user to have more than one plausible solution. Also, He al @cite_33 propose an exemplar-based colorization method using a deep learning approach. The colorization network jointly learns faithful local colorization to a meaningful reference and plausible color prediction when a reliable reference is unavailable.