Grayscale Image Colorization with GAN and CycleGAN in Different Image Domain
This is an incremental improvement for image processing applications, addressing colorization in specific domains like faces and comics.
The paper tackled automatic grayscale image colorization by reproducing a GAN-based model and proposing a CycleGAN variant, finding that the CycleGAN model performed well in human-face and comic coloring but lacked diversity in colorization.
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.