A Large-scale Film Style Dataset for Learning Multi-frequency Driven Film Enhancement

TL;DR

This paper introduces FilmSet, a large-scale dataset of high-resolution film images, and proposes FilmNet, a multi-frequency image stylization framework that outperforms existing methods in film-style enhancement.

Contribution

The paper presents a new high-quality film style dataset and a novel Laplacian Pyramid-based model for film image stylization, advancing the field of film-inspired image enhancement.

Findings

FilmNet outperforms state-of-the-art techniques in stylization quality.

FilmSet enables more effective research in film-style image enhancement.

The proposed method achieves superior results across multiple film types.

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