---
license: cc-by-nc-4.0
tags:
- LLIE
- low-light
- denoising
- real-world
---

# [WACV'26] Low-light Smartphone Dataset (LSD)

This is the official dataset proposed in our paper titled **"Illuminating Darkness: Learning to Enhance Low-light Images In-the-Wild"**

📄 **Paper:** [arXiv](https://arxiv.org/abs/2503.06898)  
💻 **Code:** [GitHub - LSD-TFFormer](https://github.com/sharif-apu/LSD-TFFormer)

## Overview

We introduce **LSD**, the largest in-the-wild Single-Shot Low-Light Image Enhancement (SLLIE) dataset to date.

## Dataset Structure

This repository contains the following training data files:

- `patch_DLL_gtPatch.tar.gz`
- `patch_DLL_inputPatch.tar.gz`
- `patch_NLL_gtPatch.tar.gz`
- `patch_NLL_inputPatch.tar.gz`

### Categories

- **DLL (Denoised Low-Light):** For low-light enhancement training
- **NLL (Noisy Low-Light):** For joint denoising + enhancement training

### File Organization

- `inputPatch`: Low-light input images
- `gtPatch`: Ground truth (well-lit) reference images

## Usage

Extract the archives to access the training patches:

```bash
tar -xzf patch_DLL_gtPatch.tar.gz
tar -xzf patch_DLL_inputPatch.tar.gz
tar -xzf patch_NLL_gtPatch.tar.gz
tar -xzf patch_NLL_inputPatch.tar.gz
```

## Dataset Status

✅ **Training Dataset:** Available (current files)  
🔄 **Test Dataset:** Coming soon


## Citation
You can cite our preprint as:

```bibtex
@article{sharif2025illuminating,
  title={Illuminating darkness: Enhancing real-world low-light scenes with smartphone images},
  author={Sharif, SMA and Rehman, Abdur and Abidin, Zain Ul and Naqvi, Rizwan Ali and Dharejo, Fayaz Ali and Timofte, Radu},
  journal={arXiv preprint arXiv:2503.06898},
  year={2025}
}
```