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Grayscale Image Colorization with GAN and CycleGAN in Different Image Domain

arXiv 2401.11425 ↗First posted 21 Jan 2024cs.CV cs.LGcited by 0

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Abstract (arXiv)

Automatic colorization of grayscale image has been a challenging task. Previous research have applied supervised methods in conquering this problem [ 1]. In this paper, we reproduces a GAN-based coloring model, and experiments one of its variant. We also proposed a CycleGAN based model and experiments those methods on various datasets. The result shows that the proposed CycleGAN model does well in human-face coloring and comic coloring, but lack the ability to diverse colorization.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 5 of its 10 references that are in the map. First posted on arXiv 21 Jan 2024.

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5 of 10 references.

References · 5

  1. 2015LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loopcited by 1,350
  2. 2017Wasserstein GANcited by 635
  3. 2016Learning Diverse Image Colorizationcited by 43
  4. 2017Unsupervised Diverse Colorization via Generative Adversarial Networkscited by 30
  5. 2018Image Colorization with Generative Adversarial Networkscited by 24

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