Update README.md

README.md

@@ -1,3 +1,162 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+tags:
+- infrared-small-target-detection
+- remote-sensing
+- computer-vision
+- frequency-domain
+- pytorch
+---
+
+<a id="top"></a>
+<div align="center">
+  <h1>🚀 HDNet: A Hybrid Domain Network with Multi-Scale High-Frequency Information Enhancement for Infrared Small Target Detection</h1>
+
+  <p>
+    <b>Mingzhu Xu</b><sup>1</sup>&nbsp;
+    <b>Chenglong Yu</b><sup>1</sup>&nbsp;
+    <b>Zexuan Li</b><sup>1</sup>&nbsp;
+    <b>Haoyu Tang</b><sup>1</sup>&nbsp;
+    <b>Yupeng Hu</b><sup>1✉</sup>&nbsp;
+    <b>Liqiang Nie</b><sup>1</sup>
+  </p>
+
+  <p>
+    <sup>1</sup>Affiliation (Please update if needed)
+  </p>
+</div>
+
+Official implementation of **HDNet**, a Hybrid Domain Network for Infrared Small Target Detection (IRSTD).
+
+🔗 **Journal:** IEEE Transactions on Geoscience and Remote Sensing (TGRS), 2025  
+🔗 **Task:** Infrared Small Target Detection (IRSTD)  
+🔗 **Framework:** PyTorch  
+
+---
+
+## 📌 Model Information
+
+### 1. Model Name
+**HDNet** (Hybrid Domain Network)
+
+---
+
+### 2. Task Type & Applicable Tasks
+- **Task Type:** Infrared Small Target Detection / Remote Sensing  
+- **Core Task:** Small target detection under complex backgrounds  
+- **Applicable Scenarios:**
+  - Infrared surveillance  
+  - Remote sensing target detection  
+  - Low-SNR object detection  
+
+---
+
+### 3. Project Introduction
+
+Infrared small target detection is challenging due to low signal-to-noise ratio and complex background interference.
+
+**HDNet** proposes a Hybrid Domain Network that integrates spatial-domain and frequency-domain representations:
+
+- **Spatial Domain Branch:** introduces Multi-scale Atrous Contrast (MAC) module to enhance target perception  
+- **Frequency Domain Branch:** introduces Dynamic High-Pass Filter (DHPF) to suppress low-frequency background  
+- Combines complementary representations to improve target-background contrast  
+
+### Key Contributions:
+- A hybrid-domain framework combining spatial and frequency information  
+- MAC module for multi-scale small target perception  
+- DHPF module for adaptive low-frequency suppression  
+- Extensive validation on three benchmark datasets  
+
+---
+
+### 4. Training Data Source
+
+Datasets:
+
+- **IRSTD-1K**
+- **NUAA-SIRST**
+- **NUDT-SIRST**
+
+Download datasets and place them in:
+
+```
+./datasets
+```
+
+---
+
+## 🚀 Environment Setup
+
+- Ubuntu 22.04  
+- Python 3.10  
+- PyTorch 2.1.0  
+- Torchvision 0.16.2+cu121  
+- CUDA 12.1  
+- GPU: NVIDIA RTX 3090  
+
+---
+
+## 🚀 Training
+
+```bash
+python main.py --dataset-dir './dataset/IRSTD-1k' --batch-size 4 --epochs 800 --mode 'train'
+```
+
+---
+
+## 🚀 Testing
+
+```bash
+python main.py --dataset-dir './dataset/IRSTD-1k' --batch-size 4 --mode 'test' --weight-path './weight/irstd.pkl'
+```
+
+---
+
+## 📊 Quantitative Results
+
+| Dataset    | mIoU | Pd | Fa |
+|-----------|------|----|----|
+| IRSTD-1K   | 70.26 | 94.56 | 4.33 |
+| NUAA-SIRST | 79.17 | 100 | 0.53 |
+| NUDT-SIRST | 85.17 | 98.52 | 2.78 |
+
+---
+
+## 📊 Qualitative Results
+
+Visual results:
+
+https://drive.google.com/drive/folders/1RfoxhoHpjfbRMZHBOvISrJSB5lpoz40t?usp=drive_link
+
+---
+
+## ⚠️ Notes
+
+- Based on improvements over MSHNet  
+- Uses SLS loss  
+- Designed for research purposes  
+
+---
+
+## 📝 Citation
+
+```bibtex
+@ARTICLE{11017756,
+ author={Xu, Mingzhu and Yu, Chenglong and Li, Zexuan and Tang, Haoyu and Hu, Yupeng and Nie, Liqiang},
+ journal={IEEE Transactions on Geoscience and Remote Sensing}, 
+ title={HDNet: A Hybrid Domain Network With Multiscale High-Frequency Information Enhancement for Infrared Small-Target Detection}, 
+ year={2025},
+ volume={63},
+ pages={1-15},
+ doi={10.1109/TGRS.2025.3574962}
+}
+```
+
+---
+
+## 📬 Contact
+
+For questions or collaboration, please contact the corresponding author.
+
+---
+