# CuDi: Curve Distillation for Efficient and Controllable Exposure Adjustment

arXiv: https://arxiv.org/abs/2207.14273 · HTML: https://mlsanity.com/paper/2207.14273/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 56 references that are in the map.
- First posted on arXiv 28 Jul 2022.

## Builds on (most cited first, 17 of 17)

- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [Deep Retinex Decomposition for Low-Light Enhancement](https://mlsanity.com/paper/1808.04560/) (2018)
- [Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement](https://mlsanity.com/paper/2001.06826/) (2020)
- [EnlightenGAN: Deep Light Enhancement without Paired Supervision](https://mlsanity.com/paper/1906.06972/) (2019)
- [Kindling the Darkness: A Practical Low-light Image Enhancer](https://mlsanity.com/paper/1905.04161/) (2019)
- [Retinex-inspired Unrolling with Cooperative Prior Architecture Search for Low-light Image Enhancement](https://mlsanity.com/paper/2012.05609/) (2020)
- [LLNet: A Deep Autoencoder Approach to Natural Low-light Image Enhancement](https://mlsanity.com/paper/1511.03995/) (2015)
- [From Patches to Pictures (PaQ-2-PiQ): Mapping the Perceptual Space of Picture Quality](https://mlsanity.com/paper/1912.10088/) (2019)
- [Deep Bilateral Learning for Real-Time Image Enhancement](https://mlsanity.com/paper/1707.02880/) (2017)
- [Learning to Enhance Low-Light Image via Zero-Reference Deep Curve Estimation](https://mlsanity.com/paper/2103.00860/) (2021)
- [Learning Image-adaptive 3D Lookup Tables for High Performance Photo Enhancement in Real-time](https://mlsanity.com/paper/2009.14468/) (2020)
- [The 2018 PIRM Challenge on Perceptual Image Super-resolution](https://mlsanity.com/paper/1809.07517/) (2018)
- [Low-Light Image and Video Enhancement Using Deep Learning: A Survey](https://mlsanity.com/paper/2104.10729/) (2021)
- [LEDNet: Joint Low-light Enhancement and Deblurring in the Dark](https://mlsanity.com/paper/2202.03373/) (2022)
- [Adaptive Unfolding Total Variation Network for Low-Light Image Enhancement](https://mlsanity.com/paper/2110.00984/) (2021)
- [Learning to Adapt to Light](https://mlsanity.com/paper/2202.08098/) (2022)

## Built on by (most cited first, 11 of 11)

- [Zero-Reference Low-Light Enhancement via Physical Quadruple Priors](https://mlsanity.com/paper/2403.12933/) (2024)
- [AGLLDiff: Guiding Diffusion Models Towards Unsupervised Training-free Real-world Low-light Image Enhancement](https://mlsanity.com/paper/2407.14900/) (2024)
- [DiffRetouch: Using Diffusion to Retouch on the Shoulder of Experts](https://mlsanity.com/paper/2407.03757/) (2024)
- [Searching a Compact Architecture for Robust Multi-Exposure Image Fusion](https://mlsanity.com/paper/2305.12236/) (2023)
- [PhotoArtAgent: Intelligent Photo Retouching with Language Model-Based Artist Agents](https://mlsanity.com/paper/2505.23130/) (2025)
- [PIE: Physics-inspired Low-light Enhancement](https://mlsanity.com/paper/2404.04586/) (2024)
- [ECAFormer: Low-light Image Enhancement using Cross Attention](https://mlsanity.com/paper/2406.13281/) (2024)
- [PASDiff: Physics-Aware Semantic Guidance for Joint Real-World Low-Light Face Enhancement and Restoration](https://mlsanity.com/paper/2603.24969/) (2026)
- [LUT-GCE: Lookup Table Global Curve Estimation for Fast Low-light Image Enhancement](https://mlsanity.com/paper/2306.07083/) (2023)
- [High-Quality Exposure Correction with Diffusion-Based Image Generation Priors](https://mlsanity.com/paper/2608.08720/) (2026)
- [Dotting the Eye: An Intent-Driven Image Retouching Agent for Visual Focus Enhancement](https://mlsanity.com/paper/2609.01148/) (2026)

## Abstract (arXiv)

We present Curve Distillation, CuDi, for efficient and controllable exposure adjustment without the requirement of paired or unpaired data during training. Our method inherits the zero-reference learning and curve-based framework from an effective low-light image enhancement method, Zero-DCE, with further speed up in its inference speed, reduction in its model size, and extension to controllable exposure adjustment. The improved inference speed and lightweight model are achieved through novel curve distillation that approximates the time-consuming iterative operation in the conventional curve-based framework by high-order curve's tangent line. The controllable exposure adjustment is made possible with a new self-supervised spatial exposure control loss that constrains the exposure levels of different spatial regions of the output to be close to the brightness distribution of an exposure map serving as an input condition. Different from most existing methods that can only correct either underexposed or overexposed photos, our approach corrects both underexposed and overexposed photos with a single model. Notably, our approach can additionally adjust the exposure levels of a photo globally or locally with the guidance of an input condition exposure map, which can be pre-defined or manually set in the inference stage. Through extensive experiments, we show that our method is appealing for its fast, robust, and flexible performance, outperforming state-of-the-art methods in real scenes. Project page: https://li-chongyi.github.io/CuDi_files/.