| ---
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| license: mit
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| task_categories:
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| - image-to-image
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| tags:
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| - image-super-resolution
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| - diffusion
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| - pytorch
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| configs:
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| - config_name: imagenet512
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| data_files: "imagenet512/**"
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| - config_name: RealSR
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| data_files: "RealSR/**"
|
| ---
|
| # 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)
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| - **GitHub Repository:** [Faze-Hsw/LPNSR](https://github.com/Faze-Hsw/LPNSR)
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|
|
| ## Dataset Description
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|
|
| 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.
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|
|
| 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.
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|
|
| ### Sub-Datasets Included:
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|
|
| - **`imagenet512`**: Contains 3,000 synthetic image pairs used for validation/testing.
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| - **`RealSR`**: Contains 100 image pairs featuring real-world degradations captured from actual camera sensors.
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| - **`RealSet80`**: Contains 80 real-world highly degraded images without Ground-Truth references.
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|
|
| ## How to Use
|
|
|
| Load the paired super-resolution datasets (`imagenet512`, `RealSR`) using the Hugging Face `datasets` library:
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|
|
| ```python
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| from datasets import load_dataset
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|
|
| # Load the imagenet512 subset
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| dataset_imagenet = load_dataset("mirpri/LPNSR-dataset", name="imagenet512")
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|
|
| # Load the RealSR subset
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| dataset_realsr = load_dataset("mirpri/LPNSR-dataset", name="RealSR")
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|
|
| # Check the properties of the first pair
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| print(dataset_imagenet['train'][0])
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| # Keys will map to 'image' (for LQ) and 'ground_truth' (for GT).
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| ```
|
|
|
| ## Citation
|
|
|
| If you find this dataset or the LPNSR framework useful, please cite our paper:
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|
|
| ```bibtex
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| @article{lpnsr2026,
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| title={LPNSR: Prior-Enhanced Diffusion Image Super-Resolution via LR-Guided Noise Prediction},
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| author={Huang, Shuwei and Liu, Shizhuo and Wei, Zijun},
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| journal={arXiv preprint arXiv:2603.21045},
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| year={2026},
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| eprint={2603.21045},
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| archivePrefix={arXiv},
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| primaryClass={cs.CV}
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| }
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| ```
|
|
|
| ## 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). |
