Details

Autor(en) / Beteiligte

• Vitoria, Patricia
• Raad, Lara
• Ballester, Coloma

Titel

ChromaGAN: Adversarial Picture Colorization with Semantic Class Distribution

Ist Teil von

• 2020 IEEE Winter Conference on Applications of Computer Vision (WACV), 2020, p.2434-2443

Ort / Verlag

IEEE

Erscheinungsjahr

2020

Link zum Volltext

• IEEE_Xplore

Quelle

IEEE

Beschreibungen/Notizen

• The colorization of grayscale images is an ill-posed problem, with multiple correct solutions. In this paper, we propose an adversarial learning colorization approach coupled with semantic information. A generative network is used to infer the chromaticity of a given grayscale image conditioned to semantic clues. 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 state-of-the-art results.