# Flexible Piecewise Curves Estimation for Photo Enhancement

arXiv: https://arxiv.org/abs/2010.13412 · HTML: https://mlsanity.com/paper/2010.13412/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Oct 2020.

## Built on by (most cited first, 13 of 13)

- [Low-Light Image and Video Enhancement Using Deep Learning: A Survey](https://mlsanity.com/paper/2104.10729/) (2021)
- [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)
- [DiffRetouch: Using Diffusion to Retouch on the Shoulder of Experts](https://mlsanity.com/paper/2407.03757/) (2024)
- [PhotoArtAgent: Intelligent Photo Retouching with Language Model-Based Artist Agents](https://mlsanity.com/paper/2505.23130/) (2025)
- [Learning Diverse Tone Styles for Image Retouching](https://mlsanity.com/paper/2207.05430/) (2022)
- [Neural Color Operators for Sequential Image Retouching](https://mlsanity.com/paper/2207.08080/) (2022)
- [Learning Adaptive Lighting via Channel-Aware Guidance](https://mlsanity.com/paper/2412.01493/) (2024)
- [High-resolution Photo Enhancement in Real-time: A Laplacian Pyramid Network](https://mlsanity.com/paper/2510.11613/) (2025)
- [Multi-Curve Translator for High-Resolution Photorealistic Image Translation](https://mlsanity.com/paper/2203.07756/) (2022)
- [Region-Aware Portrait Retouching with Sparse Interactive Guidance](https://mlsanity.com/paper/2304.04017/) (2023)
- [Learning Pixel-adaptive Multi-layer Perceptrons for Real-time Image Enhancement](https://mlsanity.com/paper/2507.12135/) (2025)

## Abstract (arXiv)

This paper presents a new method, called FlexiCurve, for photo enhancement. Unlike most existing methods that perform image-to-image mapping, which requires expensive pixel-wise reconstruction, FlexiCurve takes an input image and estimates global curves to adjust the image. The adjustment curves are specially designed for performing piecewise mapping, taking nonlinear adjustment and differentiability into account. To cope with challenging and diverse illumination properties in real-world images, FlexiCurve is formulated as a multi-task framework to produce diverse estimations and the associated confidence maps. These estimations are adaptively fused to improve local enhancements of different regions. Thanks to the image-to-curve formulation, for an image with a size of 512*512*3, FlexiCurve only needs a lightweight network (150K trainable parameters) and it has a fast inference speed (83FPS on a single NVIDIA 2080Ti GPU). The proposed method improves efficiency without compromising the enhancement quality and losing details in the original image. The method is also appealing as it is not limited to paired training data, thus it can flexibly learn rich enhancement styles from unpaired data. Extensive experiments demonstrate that our method achieves state-of-the-art performance on photo enhancement quantitively and qualitatively.