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README.md

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-license: cc-by-4.0
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+# GEWDiff Training & Evaluation Dataset
+
+## 📘 Overview
+
+The **GEWDiff Training & Evaluation Dataset** is derived from the EnMAP Champion and MDAS hyperspectral datasets.  
+It is designed for image enhancement, super-resolution, restoration, and generative remote sensing tasks.  
+The dataset includes Low-Quality (LQ) low-resolution images, corresponding Ground-Truth (GT) high-resolution images, and optional structure information such as **masks** and **edges** (partially provided; remaining components can be automatically generated using the accompanying GitHub scripts).
+
+All data have been preprocessed, spatially tiled, spectrally unified, and harmonized through **nearest-neighbor approximation of the spectral response functions (SRF)**.
+
+---
+
+## 📂 Dataset Structure
+
+### **1. Training Set**
+- **LQ images**: low-quality / low-resolution observations  
+- **GT images**: high-quality ground-truth targets  
+- **Mask (partial)**: missing parts can be generated with included scripts  
+- **Edge (partial)**: missing parts can be generated with included scripts  
+Used for model training across various reconstruction and generative tasks.
+
+---
+
+### **2. Validation Set (val)**
+- Same structure as the training set  
+- Paired LQ–GT samples for model validation and tuning
+
+---
+
+### **3. Test Sets (with ground truth)**
+Includes several subsets:
+
+- **MDAS1**  
+- **MDAS2**  
+- **WDC**
+
+These subsets contain paired LQ–GT data and are suitable for quantitative evaluations.
+
+---
+
+### **4. Test EnMAP (no ground truth)**
+- No GT is available  
+- Used only for qualitative, visual performance comparison  
+- Not intended for quantitative benchmarking
+
+---
+
+## 📐 Preprocessing Details
+
+The dataset originates from **EnMAP Champion** and **MDAS hyperspectral** sources.  
+All data have undergone:
+
+- Spatial tiling  
+- Spectral band unification  
+- **Spectral response harmonization using nearest-neighbor approximation**  
+- Conversion into LQ/GT pairs suitable for super-resolution, enhancement, and generative modeling tasks
+
+---
+
+## 🔧 Additional Resources
+
+Mask and edge maps—when not provided—can be generated automatically using the scripts available in the linked GitHub repository.  
+These structural cues enable models to leverage both texture and geometric information.
+
+---
+
+## 📑 Citation
+
+If you use this dataset in your research or applications, please cite **our paper** (arXiv](https://arxiv.org/abs/2511.07103)):
+
+```bibtex
+@misc{wang2025gewdiffgeometricenhancedwaveletbased,
+      title={GEWDiff: Geometric Enhanced Wavelet-based Diffusion Model for Hyperspectral Image Super-resolution}, 
+      author={Sirui Wang and Jiang He and Natàlia Blasco Andreo and Xiao Xiang Zhu},
+      year={2025},
+      eprint={2511.07103},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2511.07103}, 
+}
+
+---
+license: cc-by-4.0
+---