add datasets and dataset card

README.md

@@ -1,3 +1,80 @@
 ---
 license: mit
+task_categories:
+- image-to-image
+tags:
+- image-super-resolution
+- diffusion
+- pytorch
+dataset_info:
+  - config_name: imagenet512
+    features:
+      - name: image
+        dtype: image
+      - name: ground_truth
+        dtype: image
+  - config_name: RealSR
+    features:
+      - name: image
+        dtype: image
+      - name: ground_truth
+        dtype: image
 ---
+# LPNSR: Prior-Enhanced Diffusion Image Super-Resolution Dataset
+
+This repository contains the evaluation datasets and testing data associated with the paper **[LPNSR: Prior-Enhanced Diffusion Image Super-Resolution via LR-Guided Noise Prediction](https://huggingface.co/papers/2603.21045)**.
+
+## Project Links
+
+- **Paper:** [arXiv:2603.21045](https://arxiv.org/abs/2603.21045)
+- **GitHub Repository:** [Faze-Hsw/LPNSR](https://github.com/Faze-Hsw/LPNSR)
+
+## Dataset Description
+
+This dataset collection is used to evaluate image super-resolution models on both synthetic and complex real-world degradations. It contains pairs of Low-Quality (LQ) and Ground-Truth (GT) high-resolution images.
+
+The LPNSR approach utilizes an **LR-guided multi-input-aware noise predictor** instead of random Gaussian noise for partial diffusion initialization, allowing for efficient 4-step inference.
+
+### Sub-Datasets Included:
+
+- **`imagenet512`**: Contains 3,000 synthetic image pairs used for validation/testing.
+- **`RealSR`**: Contains 100 image pairs featuring real-world degradations captured from actual camera sensors.
+- **`RealSet80`**: Contains 80 real-world highly degraded images without Ground-Truth references.
+
+## How to Use
+
+Load the paired super-resolution datasets (`imagenet512`, `RealSR`) using the Hugging Face `datasets` library:
+
+```python
+from datasets import load_dataset
+
+# Load the imagenet512 subset
+dataset_imagenet = load_dataset("mirpri/LPNSR-dataset", name="imagenet512")
+
+# Load the RealSR subset
+dataset_realsr = load_dataset("mirpri/LPNSR-dataset", name="RealSR")
+
+# Check the properties of the first pair
+print(dataset_imagenet['train'][0])
+# Keys will map to 'image' (for LQ) and 'ground_truth' (for GT). 
+```
+
+## Citation
+
+If you find this dataset or the LPNSR framework useful, please cite our paper:
+
+```bibtex
+@article{lpnsr2026,
+  title={LPNSR: Prior-Enhanced Diffusion Image Super-Resolution via LR-Guided Noise Prediction},
+  author={Huang, Shuwei and Liu, Shizhuo and Wei, Zijun},
+  journal={arXiv preprint arXiv:2603.21045},
+  year={2026},
+  eprint={2603.21045},
+  archivePrefix={arXiv},
+  primaryClass={cs.CV}
+}
+```
+
+## Acknowledgement
+
+This project is based on [ResShift](https://github.com/zsyOAOA/ResShift), [BasicSR](https://github.com/XPixelGroup/BasicSR), [SwinIR](https://github.com/JingyunLiang/SwinIR), and [Real-ESRGAN](https://github.com/xinntao/Real-ESRGAN).