okhater/SICE_paired

---

license: apache-2.0

SICE Paired Low Light Image Enhancement Dataset

Overview

The SICE Paired dataset is derived from SICE Dataset Part1 available in the Awesome-Low-Light-Enhancement repository. This dataset is processed specifically for low-light image enhancement tasks by pairing under-exposed low-light input images with their corresponding ground truth images. It provides a standardized framework for training and evaluating models under challenging low-light conditions.

Dataset Composition

• Training Set: Contains paired samples where each sample consists of a low-light input image and its corresponding ground truth image.
• Test Set: Samples reserved for testing. For each sample, the low-light input image is selected based on the -1 EV exposure setting (i.e., the third image in a 7-image set or the fourth image in a 9-image set) and paired with its ground truth.

Folder Structure

The dataset is organized as follows:

SICE_Paired/ 
    ├── train/ 
        │ └── samples/ # Paired samples for training 
            │ ├── sample_001/ │ 
                │ ├── GT.jpg # Ground Truth image 
                │ │ └── Input.jpg # Low-Light input image (under-exposed) 
            │ ├── sample_002/ │ 
                │ ├── GT.jpg │ 
                │ └── Input.jpg 
            │ └── ... 

    └── test/ 
        └── samples/ # Paired samples for testing 
            ├── sample_001/ │ 
                ├── GT.jpg │ 
                └── Input.jpg 
            └── ...

Data Source and Processing

The dataset is constructed using SICE Dataset Part1 from the Awesome-Low-Light-Enhancement repository. Each original sample contains multiple exposure images along with a corresponding ground truth image in a dedicated label folder. The processing steps include:

• Low-Light Image Selection:
  For each sample, the under-exposed image (i.e., the -1 EV image) is selected as the low-light input. Specifically, if a sample contains 7 images, the 3rd image is used; if 9 images, the 4th image is chosen.

• Pairing with Ground Truth:
  The selected low-light image is paired with its corresponding ground truth image from the label folder.

• Dataset Splitting:
  Samples are split into training, and test sets based on predefined indices, ensuring a standardized evaluation protocol.

Code Samples for Data Loading

• For examples on how to build data loaders and perform preprocessing, see load_data_loaders.py.
• For dataset loading and inspection examples, refer to load_dataset.py.

Citation

If you use this dataset in your research, please cite the following paper:

@article{Cai2018deep,
  title={Learning a Deep Single Image Contrast Enhancer from Multi-Exposure Images},
  author={Cai, Jianrui and Gu, Shuhang and Zhang, Lei},
  journal={IEEE Transactions on Image Processing},
  volume={27},
  number={4},
  pages={2049-2062},
  year={2018},
  publisher={IEEE}
}

Additionally, if you find the dataset card useful in your workflow, please consider citing the dataset card as follows:

@misc{SICE_paired_dataset_card,
  author       = {Omar Khater},
  title        = {SICE Paired Low Light Image Enhancement Dataset Card},
  howpublished = {\url{https://huggingface.co/datasets/okhater/SICE_paired}},
  year         = {2025},
  note         = {Dataset card curated for efficient access to the SICE Paired dataset}
}