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

arXiv: https://arxiv.org/abs/2301.08880 · HTML: https://mlsanity.com/paper/2301.08880/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 33 references that are in the map.
- First posted on arXiv 21 Jan 2023.

## Builds on (most cited first, 16 of 16)

- [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition](https://mlsanity.com/paper/1406.4729/) (2014)
- [Simple Baselines for Image Restoration](https://mlsanity.com/paper/2204.04676/) (2022)
- [Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement](https://mlsanity.com/paper/2001.06826/) (2020)
- [Pay Less Attention with Lightweight and Dynamic Convolutions](https://mlsanity.com/paper/1901.10430/) (2019)
- [Deep Bilateral Learning for Real-Time Image Enhancement](https://mlsanity.com/paper/1707.02880/) (2017)
- [Learning Image-adaptive 3D Lookup Tables for High Performance Photo Enhancement in Real-time](https://mlsanity.com/paper/2009.14468/) (2020)
- [DeepLPF: Deep Local Parametric Filters for Image Enhancement](https://mlsanity.com/paper/2003.13985/) (2020)
- [Towards Ghost-free Shadow Removal via Dual Hierarchical Aggregation Network and Shadow Matting GAN](https://mlsanity.com/paper/1911.08718/) (2019)
- [High-Resolution Photorealistic Image Translation in Real-Time: A Laplacian Pyramid Translation Network](https://mlsanity.com/paper/2105.09188/) (2021)
- [SepLUT: Separable Image-adaptive Lookup Tables for Real-time Image Enhancement](https://mlsanity.com/paper/2207.08351/) (2022)
- [StarEnhancer: Learning Real-Time and Style-Aware Image Enhancement](https://mlsanity.com/paper/2107.12898/) (2021)
- [Degradation-invariant Enhancement of Fundus Images via Pyramid Constraint Network](https://mlsanity.com/paper/2210.09606/) (2022)
- [Shadow-Aware Dynamic Convolution for Shadow Removal](https://mlsanity.com/paper/2205.04908/) (2022)
- [Neural Color Operators for Sequential Image Retouching](https://mlsanity.com/paper/2207.08080/) (2022)

## Built on by (most cited first, 22 of 22)

- [DOPRA: Decoding Over-accumulation Penalization and Re-allocation in Specific Weighting Layer](https://mlsanity.com/paper/2407.15130/) (2024)
- [Dual-Hybrid Attention Network for Specular Highlight Removal](https://mlsanity.com/paper/2407.12255/) (2024)
- [Devignet: High-Resolution Vignetting Removal via a Dual Aggregated Fusion Transformer With Adaptive Channel Expansion](https://mlsanity.com/paper/2308.13739/) (2023)
- [MFDNet: Multi-Frequency Deflare Network for Efficient Nighttime Flare Removal](https://mlsanity.com/paper/2406.18079/) (2024)
- [SMAFormer: Synergistic Multi-Attention Transformer for Medical Image Segmentation](https://mlsanity.com/paper/2409.00346/) (2024)
- [Test-Time Intensity Consistency Adaptation for Shadow Detection](https://mlsanity.com/paper/2410.07695/) (2024)
- [DocDeshadower: Frequency-Aware Transformer for Document Shadow Removal](https://mlsanity.com/paper/2307.15318/) (2023)
- [UWFormer: Underwater Image Enhancement via a Semi-Supervised Multi-Scale Transformer](https://mlsanity.com/paper/2310.20210/) (2023)
- [Implicit Multi-Spectral Transformer: An Lightweight and Effective Visible to Infrared Image Translation Model](https://mlsanity.com/paper/2404.07072/) (2024)
- [LensNet: An End-to-End Learning Framework for Empirical Point Spread Function Modeling and Lensless Imaging Reconstruction](https://mlsanity.com/paper/2505.01755/) (2025)
- [QEAN: Quaternion-Enhanced Attention Network for Visual Dance Generation](https://mlsanity.com/paper/2403.11626/) (2024)
- [Multi-modal Mood Reader: Pre-trained Model Empowers Cross-Subject Emotion Recognition](https://mlsanity.com/paper/2405.19373/) (2024)
- [CLIP Guided Image-perceptive Prompt Learning for Image Enhancement](https://mlsanity.com/paper/2311.03943/) (2023)
- [CameraMaster: Unified Camera Semantic-Parameter Control for Photography Retouching](https://mlsanity.com/paper/2511.21024/) (2025)
- [ShaDocFormer: A Shadow-Attentive Threshold Detector With Cascaded Fusion Refiner for Document Shadow Removal](https://mlsanity.com/paper/2309.06670/) (2023)
- [UIE-UnFold: Deep Unfolding Network with Color Priors and Vision Transformer for Underwater Image Enhancement](https://mlsanity.com/paper/2408.10653/) (2024)
- [DLEN: Dual Branch of Transformer for Low-Light Image Enhancement in Dual Domains](https://mlsanity.com/paper/2501.12235/) (2025)
- [Medical Visual Prompting (MVP): A Unified Framework for Versatile and High-Quality Medical Image Segmentation](https://mlsanity.com/paper/2404.01127/) (2024)
- [Adaptive Query Prompting for Multi-Domain Landmark Detection](https://mlsanity.com/paper/2404.01194/) (2024)
- [Forestpest-YOLO: A High-Performance Detection Framework for Small Forestry Pests](https://mlsanity.com/paper/2510.00547/) (2025)
- [ProMist-5K: A Comprehensive Dataset for Digital Emulation of Cinematic Pro-Mist Filter Effects](https://mlsanity.com/paper/2601.19295/) (2026)
- [SmartPhotoCrafter: Unified Reasoning, Generation and Optimization for Automatic Photographic Image Editing](https://mlsanity.com/paper/2604.19587/) (2026)

## Abstract (arXiv)

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 urlhttps://github.com/CXH-Research/FilmNet.