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 code and data is \url{https://github.com/CXH-Research/FilmNet}.

翻译：胶片作为一种经典的图像风格，对整个摄影行业具有重要的文化意义，因为它标志着摄影的诞生。然而，胶片摄影耗时且昂贵，因此需要一种更高效的方法来收集胶片风格的图像。迄今为止，图像增强领域涌现的众多数据集并非专门针对胶片风格。为促进基于胶片的图像风格化研究，我们构建了FilmSet——一个大规模、高质量的胶片风格数据集。该数据集包含三种不同的胶片类型和超过5000张野外高分辨率图像。受FilmSet图像特征的启发，我们提出了一种基于拉普拉斯金字塔的新框架FilmNet，用于跨频带实现图像风格化并获得胶片风格效果。实验表明，我们的模型性能优于现有最先进技术。代码和数据链接为：\url{https://github.com/CXH-Research/FilmNet}。