| --- |
| pretty_name: LERSGAN_RS_PAPER |
| task_categories: |
| - image-to-image |
| tags: |
| - remote-sensing |
| - low-light-image-enhancement |
| - lersgan |
| size_categories: |
| - 1K<n<10K |
| --- |
| |
| # LERSGAN_RS_PAPER |
|
|
| Paper-structured LersGAN dataset rebuild for low-light remote sensing image enhancement. |
|
|
| - Paper: [LersGAN: A GAN-Based Model for Low-Light Remote Sensing Image Enhancement](https://doi.org/10.1109/JSTARS.2025.3608696) |
| - GitHub: https://github.com/TianqiLi11/LersGAN |
| - Dataset: https://huggingface.co/datasets/cod-tdq/lersgan |
|
|
| ## Dataset Description |
|
|
| This dataset follows the dataset protocol described in the paper's dataset section. The training and validation splits are unpaired low-light and normal-light image pools. The test split contains paired normal-light remote-sensing images and two generated low-light sets: |
|
|
| - `RSDark1`: standard low-light degradation with brightness scaling alpha in `[0.2, 0.4]` plus Gaussian sensor noise. |
| - `RSDark2`: extreme low-light degradation with brightness scaling alpha in `[0.05, 0.2]` plus Poisson-style shot noise and Gaussian read noise. |
|
|
| The paper reports aggregate selected counts, not exact per-source selected file lists. This rebuild records the available local reconstruction and is intended for LersGAN reproduction experiments. |
|
|
| ## Structure |
|
|
| ```text |
| . |
| ├── train/ |
| │ ├── low_light/ |
| │ └── normal_light/ |
| ├── val/ |
| │ ├── low_light/ |
| │ └── normal_light/ |
| └── test/ |
| ├── normal/ |
| ├── RSDark1/ |
| └── RSDark2/ |
| ``` |
|
|
| ## Counts |
|
|
| - Train: 800 low-light images and 900 normal-light images. |
| - Validation: 100 low-light images and 100 normal-light images. |
| - Test: 500 normal-light images, 500 RSDark1 images, and 500 RSDark2 images. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @article{li2025lersgan, |
| title = {LersGAN: A GAN-Based Model for Low-Light Remote Sensing Image Enhancement}, |
| author = {Li, TianQi}, |
| journal = {IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing}, |
| year = {2025}, |
| doi = {10.1109/JSTARS.2025.3608696}, |
| publisher = {IEEE} |
| } |
| ``` |
|
|
