Update dataset card

README.md

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+---
+readme: Rethinking Artifact Mitigation in HDR Reconstruction
+license: mit
+task_categories:
+- image-to-image
+- object-detection
+- image-segmentation
+language:
+- en
+tags:
+- hdr
+- artifact-detection
+- image-restoration
+- high-dynamic-range
+pretty_name: HADataset
+size_categories:
+- 100B<n<1T
+source_datasets:
+- original
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: data/Training/**
+  - split: test
+    path: data/Test/**
+---
+
+
+# Rethinking Artifact Mitigation in HDR Reconstruction: From Detection to Optimization
+#### IEEE Transactions on Image Processing (TIP), 2025
+
+[![Paper](https://img.shields.io/badge/Paper-IEEE%20TIP-blue)](https://ieeexplore.ieee.org/document/11301923)
+[![GitHub](https://img.shields.io/badge/GitHub-Repository-black?logo=github)](https://github.com/xinyueliii/hdr-artifact-detect-optimize)
+## Dataset Description
+
+**HADataset** is the first dedicated High Dynamic Range (HDR) artifact dataset designed to address the challenge of ghosting artifacts in HDR reconstruction. It explicitly provides per-pixel artifact annotations, enabling the development of detection-aware optimization strategies.
+
+This dataset was introduced in the paper "Rethinking Artifact Mitigation in HDR Reconstruction: From Detection to Optimization". It serves two main purposes:
+1.  **Artifact Detection:** Training models (like HADetector) to localize artifacts.
+2.  **HDR Reconstruction:** providing diverse multi-exposure Low Dynamic Range (LDR) image sets for testing and training reconstruction algorithms.
+
+### Key Features
+*   **Total LDR Sets:** 1,213 diverse multi-exposure sets.
+*   **Annotated Pairs:** 1,765 HDR image pairs with per-pixel artifact annotations.
+*   **Diverse Sources:** Includes artifacts from Kalantari’s dataset, our own collected scenes, and Tel’s dataset.
+*   **Model-Agnostic:** Includes artifacts generated by various state-of-the-art models (AHDR, CA-ViT, SCTNet).
+
+## Dataset Structure
+
+The HADataset consists of two main components:
+
+### 1. HADataset-LDRsets (Source LDR Images sets)
+This component includes 1,216 LDR sets captured for HDR inference.
+*   **Training Set:** 970 sets
+*   **Test Set:** 243 sets
+
+Each set typically contains 3 exposure brackets (short, medium, long) in `.tif` format along with an `exposure.txt` file.
+
+### 2. HADataset-HDRArtifactDetection (HDR images and Annotations)
+This component is designed for the artifact detection task. It contains ground truth (GT) artifact maps and the corresponding HDR images (Tp). It is categorized into two perspectives:
+
+#### Content Perspective (3 Subsets)
+Based on the origin of the scene:
+*   `HADataset-content-Kal`: Scenes from Kalantari's dataset.
+*   `HADataset-content-Ours`: Scenes collected by the authors.
+*   `HADataset-content-Tel`: Scenes from Tel's dataset.
+
+#### Model Perspective (3 Subsets)
+Based on the model that generated the artifacts:
+*   `HADataset-content-AHDR`
+*   `HADataset-content-CaViT`
+*   `HADataset-content-SCTNet`
+
+## Citation
+
+If you use this dataset in your research, please cite our paper:
+
+```bibtex
+@ARTICLE{11301923,
+  author={Li, Xinyue and Ni, Zhangkai and Wu, Hang and Yang, Wenhan and Wang, Hanli and He, Lianghua and Kwong, Sam},
+  journal={IEEE Transactions on Image Processing}, 
+  title={Rethinking Artifact Mitigation in HDR Reconstruction: From Detection to Optimization}, 
+  year={2025},
+  volume={34},
+  pages={8435-8446},
+  doi={10.1109/TIP.2025.3642557}
+}