# Learning Semantic-Aware Knowledge Guidance for Low-Light Image Enhancement

arXiv: https://arxiv.org/abs/2304.07039 · HTML: https://mlsanity.com/paper/2304.07039/

## Key facts

- Cited by 57 papers in the map (data through 14 Oct 2026).
- Builds on 32 of its 58 references that are in the map.
- First posted on arXiv 14 Apr 2023.

## Builds on (most cited first, 30 of 32)

- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Restormer: Efficient Transformer for High-Resolution Image Restoration](https://mlsanity.com/paper/2111.09881/) (2021)
- [Deep High-Resolution Representation Learning for Visual Recognition](https://mlsanity.com/paper/1908.07919/) (2019)
- [Dual Attention Network for Scene Segmentation](https://mlsanity.com/paper/1809.02983/) (2018)
- [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)
- [Recovering Realistic Texture in Image Super-resolution by Deep Spatial Feature Transform](https://mlsanity.com/paper/1804.02815/) (2018)
- [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)
- [MAXIM: Multi-Axis MLP for Image Processing](https://mlsanity.com/paper/2201.02973/) (2022)
- [Low-Light Image Enhancement with Normalizing Flow](https://mlsanity.com/paper/2109.05923/) (2021)
- [Learning Enriched Features for Fast Image Restoration and Enhancement](https://mlsanity.com/paper/2205.01649/) (2022)
- [Low-Light Image and Video Enhancement Using Deep Learning: A Survey](https://mlsanity.com/paper/2104.10729/) (2021)
- [Semantically-Guided Representation Learning for Self-Supervised Monocular Depth](https://mlsanity.com/paper/2002.12319/) (2020)
- [DeepLPF: Deep Local Parametric Filters for Image Enhancement](https://mlsanity.com/paper/2003.13985/) (2020)
- [Blind Face Restoration via Deep Multi-scale Component Dictionaries](https://mlsanity.com/paper/2008.00418/) (2020)
- [Generative Face Completion](https://mlsanity.com/paper/1704.05838/) (2017)
- [Unsupervised Night Image Enhancement: When Layer Decomposition Meets Light-Effects Suppression](https://mlsanity.com/paper/2207.10564/) (2022)
- [AdaInt: Learning Adaptive Intervals for 3D Lookup Tables on Real-time Image Enhancement](https://mlsanity.com/paper/2204.13983/) (2022)
- [Burst Image Restoration and Enhancement](https://mlsanity.com/paper/2110.03680/) (2021)
- [Semantic-Guided Zero-Shot Learning for Low-Light Image/Video Enhancement](https://mlsanity.com/paper/2110.00970/) (2021)
- [Fine-grained Semantics-aware Representation Enhancement for Self-supervised Monocular Depth Estimation](https://mlsanity.com/paper/2108.08829/) (2021)
- [Adaptive Unfolding Total Variation Network for Low-Light Image Enhancement](https://mlsanity.com/paper/2110.00984/) (2021)
- [When Image Denoising Meets High-Level Vision Tasks: A Deep Learning Approach](https://mlsanity.com/paper/1706.04284/) (2017)
- [Abandoning the Bayer-Filter to See in the Dark](https://mlsanity.com/paper/2203.04042/) (2022)
- [Semantically Contrastive Learning for Low-light Image Enhancement](https://mlsanity.com/paper/2112.06451/) (2021)
- [Half Wavelet Attention on M-Net+ for Low-Light Image Enhancement](https://mlsanity.com/paper/2203.01296/) (2022)
- [LIME: A Method for Low-light IMage Enhancement](https://mlsanity.com/paper/1605.05034/) (2016)

## Built on by (most cited first, 30 of 57)

- [LightenDiffusion: Unsupervised Low-Light Image Enhancement with Latent-Retinex Diffusion Models](https://mlsanity.com/paper/2407.08939/) (2024)
- [Residual Denoising Diffusion Models](https://mlsanity.com/paper/2308.13712/) (2023)
- [Zero-Reference Low-Light Enhancement via Physical Quadruple Priors](https://mlsanity.com/paper/2403.12933/) (2024)
- [Towards Robust Event-guided Low-Light Image Enhancement: A Large-Scale Real-World Event-Image Dataset and Novel Approach](https://mlsanity.com/paper/2404.00834/) (2024)
- [GLARE: Low Light Image Enhancement via Generative Latent Feature based Codebook Retrieval](https://mlsanity.com/paper/2407.12431/) (2024)
- [LoRA-IR: Taming Low-Rank Experts for Efficient All-in-One Image Restoration](https://mlsanity.com/paper/2410.15385/) (2024)
- [Boosting Object Detection with Zero-Shot Day-Night Domain Adaptation](https://mlsanity.com/paper/2312.01220/) (2023)
- [Color Shift Estimation-and-Correction for Image Enhancement](https://mlsanity.com/paper/2405.17725/) (2024)
- [EvLight++: Low-Light Video Enhancement with an Event Camera: A Large-Scale Real-World Dataset, Novel Method, and More](https://mlsanity.com/paper/2408.16254/) (2024)
- [Fast High Dynamic Range Radiance Fields for Dynamic Scenes](https://mlsanity.com/paper/2401.06052/) (2024)
- [Low-Light Image Enhancement using Event-Based Illumination Estimation](https://mlsanity.com/paper/2504.09379/) (2025)
- [Enlighten Anything: When Segment Anything Model Meets Low-Light Image Enhancement](https://mlsanity.com/paper/2306.10286/) (2023)
- [DiffLLE: Diffusion-guided Domain Calibration for Unsupervised Low-light Image Enhancement](https://mlsanity.com/paper/2308.09279/) (2023)
- [Geometric-Aware Low-Light Image and Video Enhancement via Depth Guidance](https://mlsanity.com/paper/2312.15855/) (2023)
- [Boosting Image Restoration via Priors from Pre-trained Models](https://mlsanity.com/paper/2403.06793/) (2024)
- [Unsupervised Image Prior via Prompt Learning and CLIP Semantic Guidance for Low-Light Image Enhancement](https://mlsanity.com/paper/2405.11478/) (2024)
- [Scene Prior Filtering for Depth Super-Resolution](https://mlsanity.com/paper/2402.13876/) (2024)
- [DI-Retinex: Digital-Imaging Retinex Theory for Low-Light Image Enhancement](https://mlsanity.com/paper/2404.03327/) (2024)
- [HUE Dataset: High-Resolution Event and Frame Sequences for Low-Light Vision](https://mlsanity.com/paper/2410.19164/) (2024)
- [SEE: See Everything Every Time -- Adaptive Brightness Adjustment for Broad Light Range Images via Events](https://mlsanity.com/paper/2502.21120/) (2025)
- [GT-Mean Loss: A Simple Yet Effective Solution for Brightness Mismatch in Low-Light Image Enhancement](https://mlsanity.com/paper/2507.20148/) (2025)
- [ERetinex: Event Camera Meets Retinex Theory for Low-Light Image Enhancement](https://mlsanity.com/paper/2503.02484/) (2025)
- [TPGDiff: Hierarchical Triple-Prior Guided Diffusion for Image Restoration](https://mlsanity.com/paper/2601.20306/) (2026)
- [Enlighten-Your-Voice: When Multimodal Meets Zero-shot Low-light Image Enhancement](https://mlsanity.com/paper/2312.10109/) (2023)
- [SeeClear: Semantic Distillation Enhances Pixel Condensation for Video Super-Resolution](https://mlsanity.com/paper/2410.05799/) (2024)
- [From Enhancement to Understanding: Build a Generalized Bridge for Low-light Vision via Semantically Consistent Unsupervised Fine-tuning](https://mlsanity.com/paper/2507.08380/) (2025)
- [Unveiling Advanced Frequency Disentanglement Paradigm for Low-Light Image Enhancement](https://mlsanity.com/paper/2409.01641/) (2024)
- [Leveraging Content and Context Cues for Low-Light Image Enhancement](https://mlsanity.com/paper/2412.07693/) (2024)
- [Boosting HDR Image Reconstruction via Semantic Knowledge Transfer](https://mlsanity.com/paper/2503.15361/) (2025)
- [EvRWKV: A Continuous Interactive RWKV Framework for Effective Event-Guided Low-Light Image Enhancement](https://mlsanity.com/paper/2507.03184/) (2025)

## Abstract (arXiv)

Low-light image enhancement (LLIE) investigates how to improve illumination and produce normal-light images. The majority of existing methods improve low-light images via a global and uniform manner, without taking into account the semantic information of different regions. Without semantic priors, a network may easily deviate from a region's original color. To address this issue, we propose a novel semantic-aware knowledge-guided framework (SKF) that can assist a low-light enhancement model in learning rich and diverse priors encapsulated in a semantic segmentation model. We concentrate on incorporating semantic knowledge from three key aspects: a semantic-aware embedding module that wisely integrates semantic priors in feature representation space, a semantic-guided color histogram loss that preserves color consistency of various instances, and a semantic-guided adversarial loss that produces more natural textures by semantic priors. Our SKF is appealing in acting as a general framework in LLIE task. Extensive experiments show that models equipped with the SKF significantly outperform the baselines on multiple datasets and our SKF generalizes to different models and scenes well. The code is available at Semantic-Aware-Low-Light-Image-Enhancement.