csxmli2016/ASFFNet512

Enhanced Blind Face Restoration with Multi-Exemplar Images and Adaptive Spatial Feature Fusion (CVPR 2020)

This is a re-implemented version of our ASFFNet by using:

512*512 images from CelebRef-HQ dataset of our DMDNet
Degradation model from our ReDegNet

Prerequisites (I use python 3.8)

pip install -r requirements.txt

Pre-train Models

Download from the following url and put it into ./checkpoints/

BaiduNetDisk or
GoogleDrive

Testing

CUDA_VISIBLE_DEVICES=0 python main_test.py -s ./TestExamples/TestLists.txt -d ./TestExamples/TestResults

You may install the face_alignment by pip install face-alignment or conda install -c 1adrianb face_alignment

How to prepare the test data

1) Select the high-quality references (see ./TestExamples/ObamaRaw) and then crop face region using the following command:

cd TestExamples
CUDA_VISIBLE_DEVICES=0 python AlignAsFFHQ.py -s ./ObamaRaw -d ./HQReferences/Obama

2) Prepare the low-quality images (see ./TestExamples/LQRaw) and also crop face region using the following command:

cd TestExamples
CUDA_VISIBLE_DEVICES=0 python AlignAsFFHQ.py -s ./LQRaw -d ./LQCrop --no_padding

3) Detecting the facial landmarks of high-quality references by:

cd TestExamples
CUDA_VISIBLE_DEVICES=0 python FaceLandmarkDetection.py -s ./HQReferences

You can also add --check to show the face images labeled with the detected landmarks.

4) Preparing the TestLists.txt (see ./TestExamples/TestLists.txt) with the following format

'{}\t{}\t{}\n'.format([TEST_LQ_IMAGE_PATH],[ID_IMAGE_PATH],[ID_LANDMARKS_PATH])

5) Finally, running the following command for the guided face restoration

CUDA_VISIBLE_DEVICES=0 python main_test.py -s ./TestExamples/TestLists.txt -d ./TestExamples/TestResults

-s and -d represent the source and save pathes, respectively.

Some examples restored with different references on real-world low-quality images

From left to right: real-world low-quality input, high-quality reference, and restoration result

The first row for each group is restored with our selected optimal reference which is more consistent with the low-quality input (see poses and mouth open or closed). With our optimal reference, results are more photo-realistic and consistent with the identity (see the identity-belonging details in red boxes).

Citation of this work

@InProceedings{Li_2020_CVPR,
author = {Li, Xiaoming and Li, Wenyu and Ren, Dongwei and Zhang, Hongzhi and Wang, Meng and Zuo, Wangmeng},
title = {Enhanced Blind Face Restoration with Multi-Exemplar Images and Adaptive Spatial Feature Fusion},
booktitle = {CVPR},
year = {2020}
}

Our other blind face restoration related works:

(1) Single Reference based Specific Restoration (GFRNet)

@InProceedings{Li_2018_ECCV,
author = {Li, Xiaoming and Liu, Ming and Ye, Yuting and Zuo, Wangmeng and Lin, Liang and Yang, Ruigang},
title = {Learning Warped Guidance for Blind Face Restoration},
booktitle = {ECCV},
year = {2018}
}

(2) Multiple Exemplar based Specific Restoration (this work ASFFNet)

@InProceedings{Li_2020_CVPR,
author = {Li, Xiaoming and Li, Wenyu and Ren, Dongwei and Zhang, Hongzhi and Wang, Meng and Zuo, Wangmeng},
title = {Enhanced Blind Face Restoration with Multi-Exemplar Images and Adaptive Spatial Feature Fusion},
booktitle = {CVPR},
year = {2020}
}

(3) Component Dictionary based Generic Restoration (DFDNet)

@InProceedings{Li_2020_ECCV,
author = {Li, Xiaoming and Chen, Chaofeng and Zhou, Shangchen and Lin, Xianhui and Zuo, Wangmeng and Zhang, Lei},
title = {Blind Face Restoration via Deep Multi-scale Component Dictionaries},
booktitle = {ECCV},
year = {2020}
}

(4) Dual Memory based Generic and Specific Restoration (DMDNet)

@article{li2022learning,
title = {Learning Dual Memory Dictionaries for Blind Face Restoration},
author = {Li, Xiaoming and Zhang, Shiguang and Zhou, Shangchen and Zhang, Lei and Zuo, Wangmeng},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
year = {2022}
}

(5) Learning Real-world Degradation from Face Images (ReDegNet)

@InProceedings{Li_2022_ReDegNet,
author = {Li, Xiaoming and Chen, Chaofeng and Lin, Xianhui and Zuo, Wangmeng and Zhang, Lei},
title = {From Face to Natural Image: Learning Real Degradation for Blind Image Super-Resolution},
booktitle = {ECCV},
year = {2022}
}

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

csxmli2016 / ASFFNet512