# LIME: A Method for Low-light IMage Enhancement

arXiv: https://arxiv.org/abs/1605.05034 · HTML: https://mlsanity.com/paper/1605.05034/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 May 2016.

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

- [Learning Semantic-Aware Knowledge Guidance for Low-Light Image Enhancement](https://mlsanity.com/paper/2304.07039/) (2023)
- [GLARE: Low Light Image Enhancement via Generative Latent Feature based Codebook Retrieval](https://mlsanity.com/paper/2407.12431/) (2024)
- [Multi-Object Tracking in the Dark](https://mlsanity.com/paper/2405.06600/) (2024)
- [Noise Self-Regression: A New Learning Paradigm to Enhance Low-Light Images Without Task-Related Data](https://mlsanity.com/paper/2211.04700/) (2022)
- [Low-light Image Enhancement by Retinex Based Algorithm Unrolling and Adjustment](https://mlsanity.com/paper/2202.05972/) (2022)
- [Fearless Luminance Adaptation: A Macro-Micro-Hierarchical Transformer for Exposure Correction](https://mlsanity.com/paper/2309.00872/) (2023)
- [Domain-Adaptive 2D Human Pose Estimation via Dual Teachers in Extremely Low-Light Conditions](https://mlsanity.com/paper/2407.15451/) (2024)
- [Exposure Correction Model to Enhance Image Quality](https://mlsanity.com/paper/2204.10648/) (2022)
- [Unsupervised HDR Image and Video Tone Mapping via Contrastive Learning](https://mlsanity.com/paper/2303.07327/) (2023)
- [Decoupled Cross-Scale Cross-View Interaction for Stereo Image Enhancement in The Dark](https://mlsanity.com/paper/2211.00859/) (2022)
- [GM-MoE: Low-Light Enhancement with Gated-Mechanism Mixture-of-Experts](https://mlsanity.com/paper/2503.07417/) (2025)
- [Deep Perceptual Enhancement for Medical Image Analysis](https://mlsanity.com/paper/2503.08027/) (2025)
- [Unsupervised Methods for Video Quality Improvement: A Survey of Restoration and Enhancement Techniques](https://mlsanity.com/paper/2507.08375/) (2025)
- [CNN Injected Transformer for Image Exposure Correction](https://mlsanity.com/paper/2309.04366/) (2023)
- [Dimma: Semi-supervised Low Light Image Enhancement with Adaptive Dimming](https://mlsanity.com/paper/2310.09633/) (2023)
- [Learning Reference-Guided Exposure Correction with Hybrid Illumination Characteristics](https://mlsanity.com/paper/2605.26729/) (2026)

## Abstract (arXiv)

When one captures images in low-light conditions, the images often suffer from low visibility. This poor quality may significantly degrade the performance of many computer vision and multimedia algorithms that are primarily designed for high-quality inputs. In this paper, we propose a very simple and effective method, named as LIME, to enhance low-light images. More concretely, the illumination of each pixel is first estimated individually by finding the maximum value in R, G and B channels. Further, we refine the initial illumination map by imposing a structure prior on it, as the final illumination map. Having the well-constructed illumination map, the enhancement can be achieved accordingly. Experiments on a number of challenging real-world low-light images are present to reveal the efficacy of our LIME and show its superiority over several state-of-the-arts.