# LYT-Net

**Repository Path**: o1o2oxxx/LYT-Net

## Basic Information

- **Project Name**: LYT-Net
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2025-07-07
- **Last Updated**: 2025-07-07

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# [SPL 2025] LYT-Net: Lightweight YUV Transformer-based Network for Low-Light Image Enhancement

<div align="center">
  
![Logo](./figs/Logo.png)

[![arXiv](https://img.shields.io/badge/arxiv-paper-179bd3)](https://arxiv.org/abs/2401.15204)
[![IEEE](https://img.shields.io/badge/IEEE-paper-blue)](https://ieeexplore.ieee.org/abstract/document/10972228)
	
[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/lyt-net-lightweight-yuv-transformer-based/low-light-image-enhancement-on-lol)](https://paperswithcode.com/sota/low-light-image-enhancement-on-lol?p=lyt-net-lightweight-yuv-transformer-based)
	
[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/lyt-net-lightweight-yuv-transformer-based/low-light-image-enhancement-on-lolv2)](https://paperswithcode.com/sota/low-light-image-enhancement-on-lolv2?p=lyt-net-lightweight-yuv-transformer-based)

[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/lyt-net-lightweight-yuv-transformer-based/low-light-image-enhancement-on-lolv2-1)](https://paperswithcode.com/sota/low-light-image-enhancement-on-lolv2-1?p=lyt-net-lightweight-yuv-transformer-based)

Ranked #1 on FLOPS(G) (3.49 GFLOPS) and Params(M) (0.045M = 45k Params)
</div>

## Updates
<!-- - `12.01.2024`: text update -->
- `09.05.2025` Check out our other works on [Low-light Image Enhancement](https://github.com/albrateanu/KANT) and [Image Denoising](https://github.com/albrateanu/AKDT)!
- `21.04.2025` LYT-Net is published as a IEEE Signal Processing Letters paper. [Link to paper](https://ieeexplore.ieee.org/abstract/document/10972228).
- `17.07.2024` Released rudimentary PyTorch implementation.
- `03.04.2024` Training code re-added and adjusted.
- `30.01.2024` arXiv pre-print available.
- `10.01.2024` Pre-trained model weights and code for training and testing are released.

## Experiment
Please check the ```TensorFlow``` and ```PyTorch``` folders for library-specific implementations.

## Results

| Dataset  | TensorFlow |           | PyTorch |           |
|:--------:|:----------:|:---------:|:-------:|:---------:|
|          | PSNR       | SSIM      | PSNR    | SSIM      |
|  LOLv1   |  27.23     |  0.853    | 26.63   |  0.836    |
| LOLv2-R  |  27.80     |  0.873    | 28.41   |  0.878    |
| LOLv2-S  |  29.39     |  0.939    | 26.72   |  0.928    |


## Citation
```
@article{brateanu2025lyt,
  author={Brateanu, Alexandru and Balmez, Raul and Avram, Adrian and Orhei, Ciprian and Ancuti, Cosmin},
  journal={IEEE Signal Processing Letters}, 
  title={LYT-NET: Lightweight YUV Transformer-based Network for Low-light Image Enhancement}, 
  year={2025},
  volume={},
  number={},
  pages={1-5},
  doi={10.1109/LSP.2025.3563125}}


@article{brateanu2024lyt,
  title={LYT-Net: Lightweight YUV Transformer-based Network for Low-Light Image Enhancement},
  author={Brateanu, Alexandru and Balmez, Raul and Avram, Adrian and Orhei, Ciprian and Cosmin, Ancuti},
  journal={arXiv preprint arXiv:2401.15204},
  year={2024}
}
```