Method Overview
Film, a classic image style, is culturally significant to the whole photographic industry since it marks the birth of photography. However, film photography is time-consuming and expensive, necessitating a more efficient method for collecting film-style photographs. Numerous datasets that have emerged in the field of image enhancement so far are not film-specific. In order to facilitate film-based image stylization research, we construct FilmSet, a large-scale and high-quality film style dataset. Our dataset includes three different film types and more than 5000 in-the-wild high resolution images. Inspired by the features of FilmSet images, we propose a novel framework called FilmNet based on Laplacian Pyramid for stylizing images across frequency bands and achieving film style outcomes. Experiments reveal that the performance of our model is superior than state-of-the-art techniques. The link of our dataset and code is here.
@inproceedings{Li_2023,
doi = {10.24963/ijcai.2023/129},
url = {https://doi.org/10.24963%2Fijcai.2023%2F129},
year = 2023,
month = {aug},
publisher = {International Joint Conferences on Artificial Intelligence Organization},
author = {Zinuo Li and Xuhang Chen and Shuqiang Wang and Chi-Man Pun},
title = {A Large-Scale Film Style Dataset for Learning Multi-frequency Driven Film Enhancement},
booktitle = {Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence}
}
This work was supported in part by the University of Macau under Grant MYRG2022-00190-FST, in part by the Science and Technology Development Fund, Macau SAR, under Grant 0034/2019/AMJ, Grant 0087/2020/A2 and Grant 0049/2021/A, in part by the National Natural Science Foundations of China under Grants 62172403 and in part by the Distinguished Young Scholars Fund of Guangdong under Grant 2021B1515020019.