An analysis: different methods about line art colorization

Abstract

We have conducted a series of studies and analyses to address the problem of line art colorization. We chose Generative Adversarial Networks (GANs), a leading neural network architecture for solving this problem, as our focus. For a large number of studies based on this architecture, we improved, applied, and analytically compared four methods, pix2pix, pix2pixHD, white-box, and scaled Fourier transform (SCFT), which can represent the mainstream problem-solving direction in the field of line colorization to the greatest extent possible. Finally, two reference quantities were introduced to quantify the results of the analysis.