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README.md

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-license: apache-2.0
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+<div align="center">
+    <a href="https://arxiv.org/abs/2508.14475"><img src="https://img.shields.io/badge/Arxiv-preprint-red"></a>
+    <a href="https://pxf0429.github.io/FGResQ/"><img src="https://img.shields.io/badge/Homepage-green"></a>
+    <a href='https://github.com/sxfly99/FGRestore/stargazers'><img src='https://img.shields.io/github/stars/sxfly99/FGRestore.svg?style=social'></a>
+</div>
+
+<h1 align="center">Fine-grained Image Quality Assessment for Perceptual Image Restoration</h1>
+
+<div align="center">
+    <a href="https://github.com/sxfly99">Xiangfei Sheng</a><sup>1*</sup>,
+    <a href="https://github.com/pxf0429">Xiaofeng Pan</a><sup>1*</sup>,
+    <a href="https://github.com/yzc-ippl">Zhichao Yang</a><sup>1</sup>,
+    <a href="https://faculty.xidian.edu.cn/cpf/">Pengfei Chen</a><sup>1</sup>,
+    <a href="https://web.xidian.edu.cn/ldli/">Leida Li</a><sup>1#</sup>
+</div>
+
+<div align="center">
+  <sup>1</sup>School of Artificial Intelligence, Xidian University
+</div>
+
+<div align="center">
+<sup>*</sup>Equal contribution. <sup>#</sup>Corresponding author. 
+</div>
+
+
+<div align="center">
+  <img src="FGResQ.png" width="800"/>
+</div>
+
+<div style="font-family: sans-serif; margin-bottom: 2em;">
+    <h2 style="border-bottom: 1px solid #eaecef; padding-bottom: 0.3em; margin-bottom: 1em;">📰 News</h2>
+    <ul style="list-style-type: none; padding-left: 0;">
+        <li style="margin-bottom: 0.8em;">
+            <strong>[2025-11-08]</strong> 🎉🎉🎉Our paper, "Fine-grained Image Quality Assessment for Perceptual Image Restoration", has been accepted to appear at AAAI 2026!
+        </li>
+        <li style="margin-bottom: 0.8em;">
+            <strong>[2025-08-20]</strong> Code and pre-trained models for FGResQ released.
+        </li>
+    </ul>
+</div>
+
+
+## Quick Start
+
+This guide will help you get started with the FGResQ inference code.
+
+### 1. Installation
+
+First, clone the repository and install the required dependencies.
+
+```bash
+git clone https://github.com/sxfly99/FGResQ.git
+cd FGResQ
+pip install -r requirements.txt
+```
+
+### 2. Download Pre-trained Weights
+
+You can download the pre-trained model weights from the following link:
+[**Download Weights (Google Drive)**](https://drive.google.com/drive/folders/10MVnAoEIDZ08Rek4qkStGDY0qLiWUahJ?usp=drive_link) or [**(Baidu Netdisk)**](https://pan.baidu.com/s/1a2IZbr_PrgZYCbUbjKLykA?pwd=9ivu)
+
+Place the downloaded files in the `weights` directory.
+
+- `FGResQ.pth`: The main model for quality scoring and ranking.
+- `Degradation.pth`: The weights for the degradation-aware task branch.
+
+Create the `weights` directory if it doesn't exist and place the files inside.
+
+```
+FGRestore/
+|-- weights/
+|   |-- FGResQ.pth
+|   |-- Degradation.pth
+|-- model/
+|   |-- FGResQ.py
+|-- requirements.txt
+|-- README.md
+```
+
+## Usage
+
+The `FGResQ` provides two main functionalities: scoring a single image and comparing a pair of images.
+
+### Initialize the Scorer
+
+First, import and initialize the `FGResQ`.
+
+```python
+from model.FGResQ import FGResQ
+
+# Path to the main model weights
+model_path = "weights/FGResQ.pth"
+
+# Initialize the inference engine
+model = FGResQ(model_path=model_path)
+```
+
+### 1. Single Image Input Mode: Quality Scoring
+
+You can get a quality score for a single image. The score typically ranges from 0 to 1, where a higher score indicates better quality.
+
+```python
+image_path = "path/to/your/image.jpg"
+quality_score = model.predict_single(image_path)
+print(f"The quality score for the image is: {quality_score:.4f}")
+```
+
+### 2. Pairwise Image Input Mode: Quality Ranking
+
+You can also compare two images to determine which one has better quality.
+
+```python
+image_path1 = "path/to/image1.jpg"
+image_path2 = "path/to/image2.jpg"
+
+comparison_result = model.predict_pair(image_path1, image_path2)
+
+# The result includes a human-readable comparison and raw probabilities
+print(f"Comparison: {comparison_result['comparison']}")
+# Example output: "Comparison: Image 1 is better"
+
+print(f"Raw output probabilities: {comparison_result['comparison_raw']}")
+# Example output: "[0.8, 0.1, 0.1]" (Probabilities for Image1 > Image2, Image2 > Image1, Image1 ≈ Image2)
+```
+## Citation
+
+If you find this work is useful, pleaes cite our paper!
+
+```bibtex
+
+@article{sheng2025fgresq,
+  title={Fine-grained Image Quality Assessment for Perceptual Image Restoration},
+  author={Sheng, Xiangfei and Pan, Xiaofeng and Yang, Zhichao and Chen, Pengfei and Li, Leida},
+  journal={arXiv preprint arXiv:2508.14475},
+  year={2025}
+}
+
+---
+license: apache-2.0
+---