Bringing Old Photo Back to Life (CVPR 2020 oral) Saturday, Nov 14, 2020


Old Photo Restoration (Official PyTorch Implementation)

Project Page | Paper (CVPR version) | Paper (Journal version) | Pretrained Model | Colab Demo

Bringing Old Photos Back to Life, CVPR2020 (Oral)

Old Photo Restoration via Deep Latent Space Translation, PAMI Under Review

Ziyu Wan1, Bo Zhang2, Dongdong Chen3, Pan Zhang4, Dong Chen2, Jing Liao1, Fang Wen2 1City University of Hong Kong, 2Microsoft Research Asia, 3Microsoft Cloud AI, 4USTC


You can now play with our Colab and try it on your photos.


The code is tested on Ubuntu with Nvidia GPUs and CUDA installed. Python>=3.6 is required to run the code.


Clone the Synchronized-BatchNorm-PyTorch repository for

cd Face_Enhancement/models/networks/
git clone
cp -rf Synchronized-BatchNorm-PyTorch/sync_batchnorm .
cd ../../../
cd Global/detection_models
git clone
cp -rf Synchronized-BatchNorm-PyTorch/sync_batchnorm .
cd ../../

Download the landmark detection pretrained model

cd Face_Detection/
bzip2 -d shape_predictor_68_face_landmarks.dat.bz2
cd ../

Download the pretrained model from Azure Blob Storage, put the file Face_Enhancement/ under ./Face_Enhancement, and put the file Global/ under ./Global. Then unzip them respectively.

cd Face_Enhancement/
cd ../
cd Global/
cd ../

Install dependencies:

pip install -r requirements.txt

How to use?

1) Full Pipeline

You could easily restore the old photos with one simple command after installation and downloading the pretrained model.

For images without scratches:

python --input_folder [test_image_folder_path] \
              --output_folder [output_path] \
              --GPU 0

For scratched images:

python --input_folder [test_image_folder_path] \
              --output_folder [output_path] \
              --GPU 0 \

Note: Please try to use the absolute path. The final results will be saved in ./output_path/final_output/. You could also check the produced results of different steps in output_path.

2) Scratch Detection

Currently we don’t plan to release the scratched old photos dataset with labels directly. If you want to get the paired data, you could use our pretrained model to test the collected images to obtain the labels.

cd Global/
python --test_path [test_image_folder_path] \
                    --output_dir [output_path] \
                    --input_size [resize_256|full_size|scale_256]

3) Global Restoration

A triplet domain translation network is proposed to solve both structured degradation and unstructured degradation of old photos.

cd Global/
python --Scratch_and_Quality_restore \
               --test_input [test_image_folder_path] \
               --test_mask [corresponding mask] \
               --outputs_dir [output_path]

python --Quality_restore \
 --test_input [test_image_folder_path] \
 --outputs_dir [output_path]

4) Face Enhancement

We use a progressive generator to refine the face regions of old photos. More details could be found in our journal submission and ./Face_Enhancement folder.

NOTE: This repo is mainly for research purpose and we have not yet optimized the running performance.

Since the model is pretrained with 256*256 images, the model may not work ideally for arbitrary resolution.

To Do

  • Clean testing code
  • Release pretrained model
  • Collab demo
  • Replace face detection module (dlib) with RetinaFace
  • Release training code


If you find our work useful for your research, please consider citing the following papers :)

title={Bringing Old Photos Back to Life},
author={Wan, Ziyu and Zhang, Bo and Chen, Dongdong and Zhang, Pan and Chen, Dong and Liao, Jing and Wen, Fang},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
Author = {Ziyu Wan and Bo Zhang and Dongdong Chen and Pan Zhang and Dong Chen and Jing Liao and Fang Wen},
Title = {Old Photo Restoration via Deep Latent Space Translation},
Year = {2020},
Eprint = {arXiv:2009.07047},


This project is currently maintained by Ziyu Wan and is for academic research use only. If you have any questions, feel free to contact [email protected].


The codes and the pretrained model in this repository are under the MIT license as specified by the LICENSE file. We use our labeled dataset to train the scratch detection model.

This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact [email protected] with any additional questions or comments.

Unauthorized reproduction of this site is prohibited, and offenders will be held accountable for their legal responsibilities.
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