Closing the Domain Gap in Manga Colorization via Aligned Paired Dataset

This paper addresses the challenge of artwork colorization by proposing a benchmark for manga colorization using real black-and-white and colorized image pairs. Color images are widely recognized for their ability to capture attention and improve memory retention, yet the manual process of colorization is labor-intensive. Deep learning methods for supervised image-to-image translation offer a promising solution, relying on aligned pairs of black-and-white and color images for training. However, these pairs are often generated synthetically, introducing a domain gap that limits model performance. To address this, we explore the use of real data, proposing a method for creating such datasets. Our benchmarks reveal that models trained on real data significantly outperform those trained on synthetic pairs. Furthermore, we present a pipeline for text removal and panel segmentation, streamlining the comic colorization process. These contributions aim to enhance the generalization and applicability of deep learning models for artwork colorization.