# Neural Preset for Color Style Transfer

arXiv: https://arxiv.org/abs/2303.13511 · HTML: https://mlsanity.com/paper/2303.13511/

## Key facts

- Cited by 36 papers in the map (data through 14 Oct 2026).
- Builds on 60 of its 80 references that are in the map.
- First posted on arXiv 23 Mar 2023.

## Builds on (most cited first, 30 of 60)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Bootstrap your own latent: A new approach to self-supervised Learning](https://mlsanity.com/paper/2006.07733/) (2020)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://mlsanity.com/paper/1603.08155/) (2016)
- [Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization](https://mlsanity.com/paper/1703.06868/) (2017)
- [Unsupervised Representation Learning by Predicting Image Rotations](https://mlsanity.com/paper/1803.07728/) (2018)
- [Context Encoders: Feature Learning by Inpainting](https://mlsanity.com/paper/1604.07379/) (2016)
- [Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzles](https://mlsanity.com/paper/1603.09246/) (2016)
- [Least Squares Generative Adversarial Networks](https://mlsanity.com/paper/1611.04076/) (2016)
- [Deep Retinex Decomposition for Low-Light Enhancement](https://mlsanity.com/paper/1808.04560/) (2018)
- [Holistically-Nested Edge Detection](https://mlsanity.com/paper/1504.06375/) (2015)
- [Benchmarking Single Image Dehazing and Beyond](https://mlsanity.com/paper/1712.04143/) (2017)
- [Universal Style Transfer via Feature Transforms](https://mlsanity.com/paper/1705.08086/) (2017)
- [A Learned Representation For Artistic Style](https://mlsanity.com/paper/1610.07629/) (2016)
- [An Underwater Image Enhancement Benchmark Dataset and Beyond](https://mlsanity.com/paper/1901.05495/) (2019)
- [Deep Bilateral Learning for Real-Time Image Enhancement](https://mlsanity.com/paper/1707.02880/) (2017)
- [StyTr2: Image Style Transfer with Transformers](https://mlsanity.com/paper/2105.14576/) (2021)
- [Fast Underwater Image Enhancement for Improved Visual Perception](https://mlsanity.com/paper/1903.09766/) (2019)
- [Deep Photo Style Transfer](https://mlsanity.com/paper/1703.07511/) (2017)
- [Photorealistic Style Transfer via Wavelet Transforms](https://mlsanity.com/paper/1903.09760/) (2019)
- [Learning Image-adaptive 3D Lookup Tables for High Performance Photo Enhancement in Real-time](https://mlsanity.com/paper/2009.14468/) (2020)
- [High-Quality Self-Supervised Deep Image Denoising](https://mlsanity.com/paper/1901.10277/) (2019)
- [Investigating Bi-Level Optimization for Learning and Vision from a Unified Perspective: A Survey and Beyond](https://mlsanity.com/paper/2101.11517/) (2021)

## Built on by (most cited first, 30 of 36)

- [NILUT: Conditional Neural Implicit 3D Lookup Tables for Image Enhancement](https://mlsanity.com/paper/2306.11920/) (2023)
- [InstaStyle: Inversion Noise of a Stylized Image is Secretly a Style Adviser](https://mlsanity.com/paper/2311.15040/) (2023)
- [Novel Object Synthesis via Adaptive Text-Image Harmony](https://mlsanity.com/paper/2410.20823/) (2024)
- [TALE: Training-free Cross-domain Image Composition via Adaptive Latent Manipulation and Energy-guided Optimization](https://mlsanity.com/paper/2408.03637/) (2024)
- [Video Color Grading via Look-Up Table Generation](https://mlsanity.com/paper/2508.00548/) (2025)
- [SA-LUT: Spatial Adaptive 4D Look-Up Table for Photorealistic Style Transfer](https://mlsanity.com/paper/2506.13465/) (2025)
- [Color Transfer with Modulated Flows](https://mlsanity.com/paper/2503.19062/) (2025)
- [Evaluation in Neural Style Transfer: A Review](https://mlsanity.com/paper/2401.17109/) (2024)
- [Color Matching Using Hypernetwork-Based Kolmogorov-Arnold Networks](https://mlsanity.com/paper/2503.11781/) (2025)
- [Deterministic Neural Illumination Mapping for Efficient Auto-White Balance Correction](https://mlsanity.com/paper/2308.03939/) (2023)
- [INRetouch: Context Aware Implicit Neural Representation for Photography Retouching](https://mlsanity.com/paper/2412.03848/) (2024)
- [ClimateGS: Real-Time Climate Simulation with 3D Gaussian Style Transfer](https://mlsanity.com/paper/2503.14845/) (2025)
- [Personalized Image Filter: Mastering Your Photographic Style](https://mlsanity.com/paper/2510.16791/) (2025)
- [StyleQoRA: Quality-Aware Low-Rank Adaptation for Few-Shot Multi-Style Editing](https://mlsanity.com/paper/2511.11236/) (2025)
- [AceTone: Bridging Words and Colors for Conditional Image Grading](https://mlsanity.com/paper/2604.00530/) (2026)
- [IEA: Amateur-Friendly Conversational Image Editing Agent via Three Stages of Multitask Alignment](https://mlsanity.com/paper/2606.08016/) (2026)
- [Optimal Image Transport on Sparse Dictionaries](https://mlsanity.com/paper/2311.01984/) (2023)
- [Learned Single-Pass Multitasking Perceptual Graphics for Immersive Displays](https://mlsanity.com/paper/2408.07836/) (2024)
- [HarmonPaint: Harmonized Training-Free Diffusion Inpainting](https://mlsanity.com/paper/2507.16732/) (2025)
- [VMDiff: Visual Mixing Diffusion for Limitless Cross-Object Synthesis](https://mlsanity.com/paper/2509.23605/) (2025)
- [T2T-VICL: Cross-Task Visual In-Context Learning via Implicit Text-Driven VLMs](https://mlsanity.com/paper/2511.16107/) (2025)
- [Experts-Guided Unbalanced Optimal Transport for ISP Learning from Unpaired and/or Paired Data](https://mlsanity.com/paper/2512.05635/) (2025)
- [Towards In-Context Tone Style Transfer with A Large-Scale Triplet Dataset](https://mlsanity.com/paper/2604.16114/) (2026)
- [CanonCGT: Reference-Based Color Grading via Canonical Pivot Representation](https://mlsanity.com/paper/2606.01638/) (2026)
- [Multimodal 3D LUT Generation via StatLUT with Statistical Features for Photorealistic Style Transfer](https://mlsanity.com/paper/2607.08227/) (2026)
- [Uni-ISP: Toward Unifying the Learning of ISPs from Multiple Mobile Cameras](https://mlsanity.com/paper/2406.01003/) (2024)
- [MRStyle: A Unified Framework for Color Style Transfer with Multi-Modality Reference](https://mlsanity.com/paper/2409.05250/) (2024)
- [Computer-aided Colorization State-of-the-science: A Survey](https://mlsanity.com/paper/2410.02288/) (2024)
- [SemiNFT: Learning to Transfer Presets from Imitation to Appreciation via Hybrid-Sample Reinforcement Learning](https://mlsanity.com/paper/2602.08582/) (2026)
- [Mitigating Content Shift and Hallucination in GenAI Image Editing via Structural Refinement](https://mlsanity.com/paper/2605.30437/) (2026)

## Abstract (arXiv)

In this paper, we present a Neural Preset technique to address the limitations of existing color style transfer methods, including visual artifacts, vast memory requirement, and slow style switching speed. Our method is based on two core designs. First, we propose Deterministic Neural Color Mapping (DNCM) to consistently operate on each pixel via an image-adaptive color mapping matrix, avoiding artifacts and supporting high-resolution inputs with a small memory footprint. Second, we develop a two-stage pipeline by dividing the task into color normalization and stylization, which allows efficient style switching by extracting color styles as presets and reusing them on normalized input images. Due to the unavailability of pairwise datasets, we describe how to train Neural Preset via a self-supervised strategy. Various advantages of Neural Preset over existing methods are demonstrated through comprehensive evaluations. Notably, Neural Preset enables stable 4K color style transfer in real-time without artifacts. Besides, we show that our trained model can naturally support multiple applications without fine-tuning, including low-light image enhancement, underwater image correction, image dehazing, and image harmonization. Project page with demos: https://zhkkke.github.io/NeuralPreset .