ViCoW: A dataset for colorization and restoration of Vietnam War imagery

Abstract

This dataset presents a curated collection of 1896 high-resolution image pairs extracted from four historically significant Vietnamese films set during the Vietnam War era. Each pair consists of an original color frame and its corresponding grayscale version, generated using the ITU-R BT.601 luminance formula. Designed to support research in historical image restoration and colorization, the dataset serves as a benchmark for evaluating AI-driven colorization techniques. Frames were systematically extracted at 3 s intervals from well-preserved archival footage, followed by manual selection to ensure visual diversity and contextual relevance. The dataset is organized into training, validation, and test sets, enabling researchers to train and assess deep learning models for restoring and colorizing historical imagery. In addition to addressing the challenges posed by aged film quality, temporal degradation, and complex visual content, this dataset contributes to digital heritage preservation by making grayscale historical visuals more accessible and engaging for modern audiences. Potential applications include the development of automated colorization systems, domain adaptation research, and AI-powered video restoration from static images.