# Deep Preset: Blending and Retouching Photos with Color Style Transfer

arXiv: https://arxiv.org/abs/2007.10701 · HTML: https://mlsanity.com/paper/2007.10701/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jul 2020.

## Built on by (most cited first, 18 of 18)

- [Neural Preset for Color Style Transfer](https://mlsanity.com/paper/2303.13511/) (2023)
- [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)
- [Semantic-guided Multi-Mask Image Harmonization](https://mlsanity.com/paper/2207.11722/) (2022)
- [Edit One for All: Interactive Batch Image Editing](https://mlsanity.com/paper/2401.10219/) (2024)
- [INRetouch: Context Aware Implicit Neural Representation for Photography Retouching](https://mlsanity.com/paper/2412.03848/) (2024)
- [Deep Photo Scan: Semi-Supervised Learning for dealing with the real-world degradation in Smartphone Photo Scanning](https://mlsanity.com/paper/2102.06120/) (2021)
- [Personalized Image Filter: Mastering Your Photographic Style](https://mlsanity.com/paper/2510.16791/) (2025)
- [AceTone: Bridging Words and Colors for Conditional Image Grading](https://mlsanity.com/paper/2604.00530/) (2026)
- [Optimal Image Transport on Sparse Dictionaries](https://mlsanity.com/paper/2311.01984/) (2023)
- [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)
- [MRStyle: A Unified Framework for Color Style Transfer with Multi-Modality Reference](https://mlsanity.com/paper/2409.05250/) (2024)
- [AuraMask: An Extensible Pipeline for Developing Aesthetic Anti-Facial Recognition Image Filters](https://mlsanity.com/paper/2605.12937/) (2026)
- [Mitigating Content Shift and Hallucination in GenAI Image Editing via Structural Refinement](https://mlsanity.com/paper/2605.30437/) (2026)
- [ColorFM: An Optimization-to-Learning Framework for Color Transfer via Flow Matching](https://mlsanity.com/paper/2607.07119/) (2026)
- [PrefLUT: Reusable and Refinable Personalized Color Editing from Pairwise Preferences](https://mlsanity.com/paper/2609.34133/) (2026)

## Abstract (arXiv)

End-users, without knowledge in photography, desire to beautify their photos to have a similar color style as a well-retouched reference. However, the definition of style in recent image style transfer works is inappropriate. They usually synthesize undesirable results due to transferring exact colors to the wrong destination. It becomes even worse in sensitive cases such as portraits. In this work, we concentrate on learning low-level image transformation, especially color-shifting methods, rather than mixing contextual features, then present a novel scheme to train color style transfer with ground-truth. Furthermore, we propose a color style transfer named Deep Preset. It is designed to 1) generalize the features representing the color transformation from content with natural colors to retouched reference, then blend it into the contextual features of content, 2) predict hyper-parameters (settings or preset) of the applied low-level color transformation methods, 3) stylize content to have a similar color style as reference. We script Lightroom, a powerful tool in editing photos, to generate 600,000 training samples using 1,200 images from the Flick2K dataset and 500 user-generated presets with 69 settings. Experimental results show that our Deep Preset outperforms the previous works in color style transfer quantitatively and qualitatively.