{"type":"ScholarlyArticle","title":"A Large-Scale Film Style Dataset for Learning Multi-frequency Driven Film Enhancement","authors":[{"name":"Zinuo Li","slug":"zinuo-li","given":"Zinuo","family":"Li","affiliation":"University of Macau"},{"name":"Xuhang Chen","slug":"xuhang-chen","given":"Xuhang","family":"Chen","affiliation":""},{"name":"Shuqiang Wang","slug":"shuqiang-wang","given":"Shuqiang","family":"Wang","affiliation":"Shenzhen Institute of Advanced Technology , Chinese Academy of Sciences"},{"name":"Chi-Man Pun","slug":"chi-man-pun","given":"Chi-Man","family":"Pun","affiliation":"University of Macau"}],"abstract":"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 https://github.com/CXH-Research/FilmNet.","doi":"10.24963/ijcai.2023/129","url":"https://ijcai.org/proceedings/2023/129","pdf":"https://ijcai.org/proceedings/2023/0129.pdf","markdown":"https://ijcai.org/proceedings/2023/129.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/129","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"Computer Vision","track":"Main Track","number":129,"pages":{"first":1160,"last":1168},"keywords":["Computer Vision: CV: Machine learning for vision","Computer Vision: CV: Computational photography"],"videos":[],"references":[{"position":1,"raw":"[Ba et al., 2016] Jimmy Lei Ba, Jamie Ryan Kiros, and Ge- offrey E Hinton. 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