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_27: 
  @cite_26: Colorization of grayscale images is a hot topic in computer vision. Previous research mainly focuses on producing a color image to recover the original one in a supervised learning fashion. However, since many colors share the same gray value, an input grayscale image could be diversely colorized while maintaining its reality. In this paper, we design a novel solution for unsupervised diverse colorization. Specifically, we leverage conditional generative adversarial networks to model the distribution of real-world item colors, in which we develop a fully convolutional generator with multi-layer noise to enhance diversity, with multi-layer condition concatenation to maintain reality, and with stride 1 to keep spatial information. With such a novel network architecture, the model yields highly competitive performance on the open LSUN bedroom dataset. The Turing test on 80 humans further indicates our generated color schemes are highly convincible.
  @cite_23: Over the last decade, the process of automatic image colorization has been of significant interest for several application areas including restoration of aged or degraded images. This problem is highly ill-posed due to the large degrees of freedom during the assignment of color information. Many of the recent developments in automatic colorization involve images that contain a common theme or require highly processed data such as semantic maps as input. In our approach, we attempt to fully generalize the colorization procedure using a conditional Deep Convolutional Generative Adversarial Network (DCGAN). The network is trained over datasets that are publicly available such as CIFAR-10 and Places365. The results between the generative model and traditional deep neural networks are compared.
summary: Some methods use GANs to colorize grayscale images. Isola al @cite_27 propose to use conditional GANs to map an input image to an output image using a U-Net based generator. They train their network by combining the @math -loss with an adapted GAN loss. An extension is proposed by Nazeri al @cite_23 generalizing the procedure to high resolution images, speeding up and stabilizing the training. Cao al @cite_26 also use conditional GANs but, to obtain diverse possible colorizations, they sample several times the input noise, which is incorporated in multiple layers in the proposed network architecture, which consists of a fully convolutional non-stride network. Their choice of the LSUN bedroom dataset helps their method to learn the diversity of bedroom colors. Notice, that none of these GANs based methods use additional information such as classification.