In this paper, we use Generative Adversarial Networks (GAN) to address the industrial needs of auto colorization of line arts which takes enormous amount of manual labor. Auto-colorization method used in Image-to-Image conversion based on GAN has received a lot of attention due to its promising results.
In this paper, we present a solution to not only colorize the line art but also transform the low resolution out image to match the resolution of the input image through 2 generators and frequency separation method. A high frequency components are extracted from the line, then 2 generators are used to colorize the image in low resolution. The high frequency component is merged with low resolution image to produce the high resolution colorized image. The resolution of final output image matches the resolution of original image while preserving the texture of the input image, whereas the other schemes reduce the output image to 512 pixels.
We performed visual and qualitative evaluation using FID, PSNR, and SSIM. The FID Score of the proposed method is better than the base model by about 4 (proposed: 47.87 and base model 51.64). PNSR and SSIM of the high-resolution images are also better than the base model. PSNR and SSIM of base model is 13.01 and 0.72 whereas the proposed is 20.77 and 0.86, respectively.
[Keywords: machine learning, Generative Adversarial Network, line arts colorization, image generation]