# Supervised and Unsupervised Learning of Parameterized Color Enhancement

arXiv: https://arxiv.org/abs/2001.05843 · HTML: https://mlsanity.com/paper/2001.05843/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Dec 2019.

## Built on by (most cited first, 16 of 16)

- [AdaInt: Learning Adaptive Intervals for 3D Lookup Tables on Real-time Image Enhancement](https://mlsanity.com/paper/2204.13983/) (2022)
- [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)
- [Progressive Joint Low-light Enhancement and Noise Removal for Raw Images](https://mlsanity.com/paper/2106.14844/) (2021)
- [Very Lightweight Photo Retouching Network with Conditional Sequential Modulation](https://mlsanity.com/paper/2104.06279/) (2021)
- [CLIPtone: Unsupervised Learning for Text-based Image Tone Adjustment](https://mlsanity.com/paper/2404.01123/) (2024)
- [Physics-Constrained Fine-Tuning of Flow-Matching Models for Generation and Inverse Problems](https://mlsanity.com/paper/2508.09156/) (2025)
- [PhotoArtAgent: Intelligent Photo Retouching with Language Model-Based Artist Agents](https://mlsanity.com/paper/2505.23130/) (2025)
- [Neural Color Operators for Sequential Image Retouching](https://mlsanity.com/paper/2207.08080/) (2022)
- [Region-Aware Portrait Retouching with Sparse Interactive Guidance](https://mlsanity.com/paper/2304.04017/) (2023)
- [TreEnhance: A Tree Search Method For Low-Light Image Enhancement](https://mlsanity.com/paper/2205.12639/) (2022)
- [HyperPose: Hypernetwork-Infused Camera Pose Localization and an Extended Cambridge Landmarks Dataset](https://mlsanity.com/paper/2303.02610/) (2023)
- [Beyond Static Models: Hypernetworks for Adaptive and Generalizable Forecasting in Complex Parametric Dynamical Systems](https://mlsanity.com/paper/2506.19609/) (2025)
- [Adaptive Guided Upsampling for Low-light Image Enhancement](https://mlsanity.com/paper/2511.16623/) (2025)
- [Edge-Aware Image Manipulation via Diffusion Models with a Novel Structure-Preservation Loss](https://mlsanity.com/paper/2601.16645/) (2026)
- [MERIT: Multi-domain Efficient RAW Image Translation](https://mlsanity.com/paper/2603.20836/) (2026)

## Abstract (arXiv)

We treat the problem of color enhancement as an image translation task, which we tackle using both supervised and unsupervised learning. Unlike traditional image to image generators, our translation is performed using a global parameterized color transformation instead of learning to directly map image information. In the supervised case, every training image is paired with a desired target image and a convolutional neural network (CNN) learns from the expert retouched images the parameters of the transformation. In the unpaired case, we employ two-way generative adversarial networks (GANs) to learn these parameters and apply a circularity constraint. We achieve state-of-the-art results compared to both supervised (paired data) and unsupervised (unpaired data) image enhancement methods on the MIT-Adobe FiveK benchmark. Moreover, we show the generalization capability of our method, by applying it on photos from the early 20th century and to dark video frames.