# LYT-NET: Lightweight YUV Transformer-based Network for Low-light Image Enhancement

arXiv: https://arxiv.org/abs/2401.15204 · HTML: https://mlsanity.com/paper/2401.15204/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 64 references that are in the map.
- First posted on arXiv 26 Jan 2024.

## Builds on (most cited first, 22 of 22)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions](https://mlsanity.com/paper/2102.12122/) (2021)
- [Restormer: Efficient Transformer for High-Resolution Image Restoration](https://mlsanity.com/paper/2111.09881/) (2021)
- [Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers](https://mlsanity.com/paper/2012.15840/) (2020)
- [Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNet](https://mlsanity.com/paper/2101.11986/) (2021)
- [Uformer: A General U-Shaped Transformer for Image Restoration](https://mlsanity.com/paper/2106.03106/) (2021)
- [Deep Retinex Decomposition for Low-Light Enhancement](https://mlsanity.com/paper/1808.04560/) (2018)
- [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)
- [Learning Enriched Features for Real Image Restoration and Enhancement](https://mlsanity.com/paper/2003.06792/) (2020)
- [Retinex-inspired Unrolling with Cooperative Prior Architecture Search for Low-light Image Enhancement](https://mlsanity.com/paper/2012.05609/) (2020)
- [Retinexformer: One-stage Retinex-based Transformer for Low-light Image Enhancement](https://mlsanity.com/paper/2303.06705/) (2023)
- [Residual Dense Network for Image Restoration](https://mlsanity.com/paper/1812.10477/) (2018)
- [Ultra-High-Definition Low-Light Image Enhancement: A Benchmark and Transformer-Based Method](https://mlsanity.com/paper/2212.11548/) (2022)
- [Learning Image-adaptive 3D Lookup Tables for High Performance Photo Enhancement in Real-time](https://mlsanity.com/paper/2009.14468/) (2020)
- [Low-Light Image Enhancement with Wavelet-based Diffusion Models](https://mlsanity.com/paper/2306.00306/) (2023)
- [Diff-Retinex: Rethinking Low-light Image Enhancement with A Generative Diffusion Model](https://mlsanity.com/paper/2308.13164/) (2023)
- [DeepLPF: Deep Local Parametric Filters for Image Enhancement](https://mlsanity.com/paper/2003.13985/) (2020)
- [Global Structure-Aware Diffusion Process for Low-Light Image Enhancement](https://mlsanity.com/paper/2310.17577/) (2023)
- [Burst Image Restoration and Enhancement](https://mlsanity.com/paper/2110.03680/) (2021)
- [Dilated filters for edge detection algorithms](https://mlsanity.com/paper/2106.07395/) (2021)

## Built on by (most cited first, 22 of 22)

- [HVI: A New Color Space for Low-light Image Enhancement](https://mlsanity.com/paper/2502.20272/) (2025)
- [You Only Need One Color Space: An Efficient Network for Low-light Image Enhancement](https://mlsanity.com/paper/2402.05809/) (2024)
- [UltraFast-LiNET: Light-weight multi-scale shift convolutional network for real-time low-light image enhancement](https://mlsanity.com/paper/2512.02965/) (2025)
- [Color Matching Using Hypernetwork-Based Kolmogorov-Arnold Networks](https://mlsanity.com/paper/2503.11781/) (2025)
- [Rethinking Theoretical Illumination for Efficient Low-Light Image Enhancement](https://mlsanity.com/paper/2409.05274/) (2024)
- [Illuminating Darkness: Learning to Enhance Low-light Images In-the-Wild](https://mlsanity.com/paper/2503.06898/) (2025)
- [From Darkness to Detail: Frequency-Aware SSMs for Low-Light Vision](https://mlsanity.com/paper/2408.09650/) (2024)
- [HVI-CIDNet+: Beyond Extreme Darkness for Low-Light Image Enhancement](https://mlsanity.com/paper/2507.06814/) (2025)
- [Multinex: Lightweight Low-light Image Enhancement via Multi-prior Retinex](https://mlsanity.com/paper/2604.10359/) (2026)
- [NTIRE 2025 Image Shadow Removal Challenge Report](https://mlsanity.com/paper/2506.15524/) (2025)
- [NTIRE 2025 Challenge on Low Light Image Enhancement: Methods and Results](https://mlsanity.com/paper/2510.13670/) (2025)
- [Experts-Guided Unbalanced Optimal Transport for ISP Learning from Unpaired and/or Paired Data](https://mlsanity.com/paper/2512.05635/) (2025)
- [LTCF-Net: A Transformer-Enhanced Dual-Channel Fourier Framework for Low-Light Image Restoration](https://mlsanity.com/paper/2411.15740/) (2024)
- [Lightweight and Interpretable Transformer via Mixed Graph Algorithm Unrolling for Traffic Forecast](https://mlsanity.com/paper/2505.13102/) (2025)
- [Wavelet-based Decoupling Framework for low-light Stereo Image Enhancement](https://mlsanity.com/paper/2507.12188/) (2025)
- [Using KL-Divergence to Focus Frequency Information in Low-Light Image Enhancement](https://mlsanity.com/paper/2509.13083/) (2025)
- [Diffusion Models for Low-Light Image Enhancement: A Multi-Perspective Taxonomy and Performance Analysis](https://mlsanity.com/paper/2510.05976/) (2025)
- [Low-light Image Enhancement with Retinex Decomposition in Latent Space](https://mlsanity.com/paper/2603.15131/) (2026)
- [DST-Net: A Dual-Stream Transformer with Illumination-Independent Feature Guidance and Multi-Scale Spatial Convolution for Low-Light Image Enhancement](https://mlsanity.com/paper/2603.16482/) (2026)
- [Lightweight Low-Light Image Enhancement via Distribution-Normalizing Preprocessing and Depthwise U-Net](https://mlsanity.com/paper/2604.11071/) (2026)
- [NTIRE 2026 Challenge on Efficient Low Light Image Enhancement: Methods and Results](https://mlsanity.com/paper/2605.02212/) (2026)
- [IDM-Net: A Lightweight Illumination-Decoupled Modulation Network for Low-Light Image Enhancement](https://mlsanity.com/paper/2609.30962/) (2026)

## Abstract (arXiv)

This letter introduces LYT-Net, a novel lightweight transformer-based model for low-light image enhancement (LLIE). LYT-Net consists of several layers and detachable blocks, including our novel blocks--Channel-Wise Denoiser (CWD) and Multi-Stage Squeeze & Excite Fusion (MSEF)--along with the traditional Transformer block, Multi-Headed Self-Attention (MHSA). In our method we adopt a dual-path approach, treating chrominance channels U and V and luminance channel Y as separate entities to help the model better handle illumination adjustment and corruption restoration. Our comprehensive evaluation on established LLIE datasets demonstrates that, despite its low complexity, our model outperforms recent LLIE methods. The source code and pre-trained models are available at https://github.com/albrateanu/LYT-Net