Update README.md

README.md

@@ -1,131 +1,131 @@
----
-configs:
-- config_name: default
-  data_files:
-    - split: train
-      path:
-        - "train/samples/*/*.jpg"
-    - split: test
-      path:
-        - "test/samples/*/*.jpg"
----
----
-license: apache-2.0
----
-# SICE Paired Low Light Image Enhancement Dataset
-
-# Overview
-
-The SICE Paired dataset is derived from the original SICE Dataset Part1 available in the [Awesome-Low-Light-Enhancement](https://github.com/Elin24/Awesome-Low-Light-Enhancement) repository. In SICE, each sample consists of multiple images captured at different exposure levels, accompanied by a corresponding ground truth image stored in a dedicated label folder.
-
-Recent developments in unsupervised and unpaired low-light image enhancement (LLIE) have highlighted the value of leveraging paired low-quality images—often created by randomly selecting two low-light samples—to guide model training and to facilitate the computation of supervised metrics during evaluation. Motivated by these advancements, this dataset is structured to support flexible data loading: users can either select two random low-light images (with seed control for reproducibility) or load all available low-light images per sample.
-
-## Dataset Composition
-
-- **Training Set:** Contains paired samples where each sample includes multiple low-light images (selected based on exposure criteria) and the corresponding ground truth image.
-- **Test Set:** Consists of samples reserved for evaluation. For testing, the under-exposure low-light input is selected as follows:
-  - If a sample contains 7 images: the 3rd image (corresponding to -1 EV) is used.
-  - If a sample contains 9 images: the 4th image (corresponding to -1 EV) is used.
-  
-  Similarly, the over-exposure input is chosen (if needed) as the 5th image in a 7-image sample or the 6th image in a 9-image sample.
-
-During dataset preparation, the following metadata was observed in SICE Dataset Part1:
-
-The testing index in Dataset_part1:
-
-4-23 28 31 33-34 37-39 46-52 55-69 75-79 100-103
-
-*Note:* Samples containing more than 9 images without a clear indication of under- or overexposure were skipped to maintain consistency.
-
-## Folder Structure
-The dataset is organized as follows:
-
-```
-SICE_Paired/ 
-    ├── train/ 
-        │ └── samples/ # Paired samples for training 
-            │ ├── sample_1/ │ 
-                │ ├── label.jpg # Ground Truth image 
-                │ ├── low1.jpg # Low-Light input image
-                │ ├── low2.jpg # Low-Light input image
-                │ └── ... 
-            │ ├── sample_2/ │ 
-                │ ├── label.jpg # Ground Truth image 
-                │ ├── low1.jpg # Low-Light input image
-                │ ├── low2.jpg # Low-Light input image
-                │ └── ...  
-
-    └── test/ 
-        └── samples/ # Paired samples for testing 
-            │ ├── sample_2/ │ 
-                │ ├── label.jpg # Ground Truth image 
-                │ ├── low1.jpg # Low-Light input image
-                │ ├── low2.jpg # Low-Light input image
-                │ └── ... 
-```
-## Data Source and Processing
-The dataset is constructed using SICE Dataset Part1 from the [Awesome-Low-Light-Enhancement](https://github.com/Elin24/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`.
-
-## Related Work 
-
-Several recent studies have successfully leveraged the SICE dataset for unsupervised low-light image enhancement tasks:
-
-```bibtex
-@inproceedings{fu2023learning,
-  title={Learning a Simple Low-Light Image Enhancer From Paired Low-Light Instances},
-  author={Fu, Zhenqi and Yang, Yan and Tu, Xiaotong and Huang, Yue and Ding, Xinghao and Ma, Kai-Kuang},
-  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
-  pages={22252--22261},
-  year={2023}
-}
-
-@InProceedings{Jiang_2024_ECCV,
-    author    = {Jiang, Hai and Luo, Ao and Liu, Xiaohong and Han, Songchen and Liu, Shuaicheng},
-    title     = {LightenDiffusion: Unsupervised Low-Light Image Enhancement with Latent-Retinex Diffusion Models},
-    booktitle = {European Conference on Computer Vision},
-    year      = {2024},
-    pages     = {}
-}
-```
-## Citation
-
-
-If you use this dataset in your research, please cite the following paper:
-
-```bibtex
-@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:
-
-```bibtex
-@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}
-}
+---
+configs:
+- config_name: default
+  data_files:
+    - split: train
+      path:
+        - "train/samples/*/*.jpg"
+    - split: test
+      path:
+        - "test/samples/*/*.jpg"
+---
+---
+license: apache-2.0
+---
+# SICE Paired Low Light Image Enhancement Dataset
+
+# Overview
+
+The SICE Paired dataset is derived from the original SICE Dataset Part1 available in the [Awesome-Low-Light-Enhancement](https://github.com/Elin24/Awesome-Low-Light-Enhancement) repository. In SICE, each sample consists of multiple images captured at different exposure levels, accompanied by a corresponding ground truth image stored in a dedicated label folder.
+
+Recent developments in unsupervised and unpaired low-light image enhancement (LLIE) have highlighted the value of leveraging paired low-quality images—often created by randomly selecting two low-light samples—to guide model training and to facilitate the computation of supervised metrics during evaluation. Motivated by these advancements, this dataset is structured to support flexible data loading: users can either select two random low-light images (with seed control for reproducibility) or load all available low-light images per sample.
+
+## Dataset Composition
+
+- **Training Set:** Contains paired samples where each sample includes multiple low-light images (selected based on exposure criteria) and the corresponding ground truth image.
+- **Test Set:** Consists of samples reserved for evaluation. For testing, the under-exposure low-light input is selected as follows:
+  - If a sample contains 7 images: the 3rd image (corresponding to -1 EV) is used.
+  - If a sample contains 9 images: the 4th image (corresponding to -1 EV) is used.
+  
+  Similarly, the over-exposure input is chosen (if needed) as the 5th image in a 7-image sample or the 6th image in a 9-image sample.
+
+During dataset preparation, the following metadata was observed in SICE Dataset Part1:
+
+The testing index in Dataset_part1:
+
+4-23 28 31 33-34 37-39 46-52 55-69 75-79 100-103
+
+*Note:* Samples containing more than 9 images without a clear indication of under- or overexposure were skipped to maintain consistency.
+
+## Folder Structure
+The dataset is organized as follows:
+
+```
+SICE_Paired/ 
+    ├── train/ 
+        │ └── samples/ # Paired samples for training 
+            │ ├── sample_1/ │ 
+                │ ├── label.jpg # Ground Truth image 
+                │ ├── low1.jpg # Low-Light input image
+                │ ├── low2.jpg # Low-Light input image
+                │ └── ... 
+            │ ├── sample_2/ │ 
+                │ ├── label.jpg # Ground Truth image 
+                │ ├── low1.jpg # Low-Light input image
+                │ ├── low2.jpg # Low-Light input image
+                │ └── ...  
+
+    └── test/ 
+        └── samples/ # Paired samples for testing 
+            │ ├── sample_2/ │ 
+                │ ├── label.jpg # Ground Truth image 
+                │ ├── low1.jpg # Low-Light input image
+                │ ├── low2.jpg # Low-Light input image
+                │ └── ... 
+```
+## Data Source and Processing
+The dataset is constructed using SICE Dataset Part1 from the [Awesome-Low-Light-Enhancement](https://github.com/Elin24/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`.
+
+## Related Work 
+
+Several recent studies have successfully leveraged the SICE dataset for unsupervised low-light image enhancement tasks:
+
+```bibtex
+@inproceedings{fu2023learning,
+  title={Learning a Simple Low-Light Image Enhancer From Paired Low-Light Instances},
+  author={Fu, Zhenqi and Yang, Yan and Tu, Xiaotong and Huang, Yue and Ding, Xinghao and Ma, Kai-Kuang},
+  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
+  pages={22252--22261},
+  year={2023}
+}
+
+@InProceedings{Jiang_2024_ECCV,
+    author    = {Jiang, Hai and Luo, Ao and Liu, Xiaohong and Han, Songchen and Liu, Shuaicheng},
+    title     = {LightenDiffusion: Unsupervised Low-Light Image Enhancement with Latent-Retinex Diffusion Models},
+    booktitle = {European Conference on Computer Vision},
+    year      = {2024},
+    pages     = {}
+}
+```
+## Citation
+
+
+If you use this dataset in your research, please cite the following paper:
+
+```bibtex
+@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:
+
+```bibtex
+@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}},
+  year         = {2025},
+  note         = {Dataset card curated for efficient access to the SICE Paired dataset}
+}
 ```