# FastLLVE: Real-Time Low-Light Video Enhancement with Intensity-Aware Lookup Table

arXiv: https://arxiv.org/abs/2308.06749 · HTML: https://mlsanity.com/paper/2308.06749/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 57 references that are in the map.
- First posted on arXiv 13 Aug 2023.

## Builds on (most cited first, 27 of 27)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Visualizing and Understanding Convolutional Networks](https://mlsanity.com/paper/1311.2901/) (2013)
- [Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising](https://mlsanity.com/paper/1608.03981/) (2016)
- [Instance Normalization: The Missing Ingredient for Fast Stylization](https://mlsanity.com/paper/1607.08022/) (2016)
- [Noise2Noise: Learning Image Restoration without Clean Data](https://mlsanity.com/paper/1803.04189/) (2018)
- [Empirical Evaluation of Rectified Activations in Convolutional Network](https://mlsanity.com/paper/1505.00853/) (2015)
- [Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution](https://mlsanity.com/paper/1704.03915/) (2017)
- [Learning Blind Video Temporal Consistency](https://mlsanity.com/paper/1808.00449/) (2018)
- [Learning Image-adaptive 3D Lookup Tables for High Performance Photo Enhancement in Real-time](https://mlsanity.com/paper/2009.14468/) (2020)
- [Fast and Accurate Image Super-Resolution with Deep Laplacian Pyramid Networks](https://mlsanity.com/paper/1710.01992/) (2017)
- [FastDVDnet: Towards Real-Time Deep Video Denoising Without Flow Estimation](https://mlsanity.com/paper/1907.01361/) (2019)
- [Noisier2Noise: Learning to Denoise from Unpaired Noisy Data](https://mlsanity.com/paper/1910.11908/) (2019)
- [Video Frame Interpolation Transformer](https://mlsanity.com/paper/2111.13817/) (2021)
- [A Closer Look at Deep Learning Heuristics: Learning rate restarts, Warmup and Distillation](https://mlsanity.com/paper/1810.13243/) (2018)
- [DeepLPF: Deep Local Parametric Filters for Image Enhancement](https://mlsanity.com/paper/2003.13985/) (2020)
- [LEDNet: Joint Low-light Enhancement and Deblurring in the Dark](https://mlsanity.com/paper/2202.03373/) (2022)
- [AdaInt: Learning Adaptive Intervals for 3D Lookup Tables on Real-time Image Enhancement](https://mlsanity.com/paper/2204.13983/) (2022)
- [Attention Guided Low-light Image Enhancement with a Large Scale Low-light Simulation Dataset](https://mlsanity.com/paper/1908.00682/) (2019)
- [Real-time Image Enhancer via Learnable Spatial-aware 3D Lookup Tables](https://mlsanity.com/paper/2108.08697/) (2021)
- [SepLUT: Separable Image-adaptive Lookup Tables for Real-time Image Enhancement](https://mlsanity.com/paper/2207.08351/) (2022)
- [Exploit Camera Raw Data for Video Super-Resolution via Hidden Markov Model Inference](https://mlsanity.com/paper/2008.10710/) (2020)
- [Learning for Unconstrained Space-Time Video Super-Resolution](https://mlsanity.com/paper/2102.13011/) (2021)
- [End-To-End Trainable Video Super-Resolution Based on a New Mechanism for Implicit Motion Estimation and Compensation](https://mlsanity.com/paper/2001.01162/) (2020)
- [DN-ResNet: Efficient Deep Residual Network for Image Denoising](https://mlsanity.com/paper/1810.06766/) (2018)
- [TransMRSR: Transformer-based Self-Distilled Generative Prior for Brain MRI Super-Resolution](https://mlsanity.com/paper/2306.06669/) (2023)
- [SALVE: Self-supervised Adaptive Low-light Video Enhancement](https://mlsanity.com/paper/2212.11484/) (2022)

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

- [GLARE: Low Light Image Enhancement via Generative Latent Feature based Codebook Retrieval](https://mlsanity.com/paper/2407.12431/) (2024)
- [Perception-Oriented Video Frame Interpolation via Asymmetric Blending](https://mlsanity.com/paper/2404.06692/) (2024)
- [Learning Hazing to Dehazing: Towards Realistic Haze Generation for Real-World Image Dehazing](https://mlsanity.com/paper/2503.19262/) (2025)
- [MoA-VR: A Mixture-of-Agents System Towards All-in-One Video Restoration](https://mlsanity.com/paper/2510.08508/) (2025)
- [AttentionLut: Attention Fusion-based Canonical Polyadic LUT for Real-time Image Enhancement](https://mlsanity.com/paper/2401.01569/) (2024)
- [Bridging Perspectives: A Survey on Cross-view Collaborative Intelligence with Egocentric-Exocentric Vision](https://mlsanity.com/paper/2506.06253/) (2025)
- [Light-VQA+: A Video Quality Assessment Model for Exposure Correction with Vision-Language Guidance](https://mlsanity.com/paper/2405.03333/) (2024)
- [Text2QR: Harmonizing Aesthetic Customization and Scanning Robustness for Text-Guided QR Code Generation](https://mlsanity.com/paper/2403.06452/) (2024)
- [OwlSight: A Robust Illumination Adaptation Framework for Dark Video Human Action Recognition](https://mlsanity.com/paper/2503.23266/) (2025)
- [Pan-LUT: Efficient Pan-sharpening via Learnable Look-Up Tables](https://mlsanity.com/paper/2503.23793/) (2025)
- [A2-Edit: Precise Reference-Guided Image Editing of Arbitrary Objects and Ambiguous Masks](https://mlsanity.com/paper/2603.10685/) (2026)
- [Optimizing 4D Lookup Table for Low-light Video Enhancement via Wavelet Priori](https://mlsanity.com/paper/2409.08585/) (2024)
- [Compression-Realized Deep Structural Network for Video Quality Enhancement](https://mlsanity.com/paper/2405.06342/) (2024)
- [RRNet: Configurable Real-Time Video Enhancement with Arbitrary Local Lighting Variations](https://mlsanity.com/paper/2601.01865/) (2026)
- [Zero-Shot Video Restoration and Enhancement with Assistance of Video Diffusion Models](https://mlsanity.com/paper/2601.21922/) (2026)
- [OLiVES: An Outdoor Low-Light Video Benchmark for Enhancement and Segmentation](https://mlsanity.com/paper/2610.20501/) (2026)

## Abstract (arXiv)

Low-Light Video Enhancement (LLVE) has received considerable attention in recent years. One of the critical requirements of LLVE is inter-frame brightness consistency, which is essential for maintaining the temporal coherence of the enhanced video. However, most existing single-image-based methods fail to address this issue, resulting in flickering effect that degrades the overall quality after enhancement. Moreover, 3D Convolution Neural Network (CNN)-based methods, which are designed for video to maintain inter-frame consistency, are computationally expensive, making them impractical for real-time applications. To address these issues, we propose an efficient pipeline named FastLLVE that leverages the Look-Up-Table (LUT) technique to maintain inter-frame brightness consistency effectively. Specifically, we design a learnable Intensity-Aware LUT (IA-LUT) module for adaptive enhancement, which addresses the low-dynamic problem in low-light scenarios. This enables FastLLVE to perform low-latency and low-complexity enhancement operations while maintaining high-quality results. Experimental results on benchmark datasets demonstrate that our method achieves the State-Of-The-Art (SOTA) performance in terms of both image quality and inter-frame brightness consistency. More importantly, our FastLLVE can process 1,080p videos at mathit50+ Frames Per Second (FPS), which is mathit2 × faster than SOTA CNN-based methods in inference time, making it a promising solution for real-time applications. The code is available at https://github.com/Wenhao-Li-777/FastLLVE.