FGResQ

<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://huggingface.co/spaces/orpheus0429/FGResQ"><img src="https://img.shields.io/badge/🤗%20Hugging%20Face-Spaces-blue"></a> <a href='https://github.com/sxfly99/FGResQ/stargazers'><img src='https://img.shields.io/github/stars/sxfly99/FGResQ.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>^1*, <a href="https://github.com/pxf0429">Xiaofeng Pan</a>^1*, <a href="https://github.com/yzc-ippl">Zhichao Yang</a>¹, <a href="https://faculty.xidian.edu.cn/cpf/">Pengfei Chen</a>¹, <a href="https://web.xidian.edu.cn/ldli/">Leida Li</a>^1# </div> <div align="center"> ¹School of Artificial Intelligence, Xidian University </div> <div align="center"> ^*Equal contribution. ^#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>[2025-11-19]</strong> The model is now available on the <a href="https://huggingface.co/orpheus0429/FGResQ">HuggingFace Hub</a>. A live demo is also available on <a href="https://huggingface.co/spaces/orpheus0429/FGResQ">HuggingFace Spaces</a> for you to try it out directly in your browser. </li> <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.

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), (Baidu Netdisk) or (HuggingFace)

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.

from model.FGResQ import FGResQ

# Path to the main model weights
model_path = "weights/FGResQ.pth"

# or use HuggingFace Model
# from huggingface_hub import hf_hub_download
# model_path = hf_hub_download(
#     repo_id="orpheus0429/FGResQ",
#     filename="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.

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.

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!

@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}
}