Coarse-to-Fine Embedded PatchMatch and Multi-Scale Dynamic Aggregation for Reference-based Super-Resolution （AAAI2022）

The code framework is mainly modified from BasicSR and MMSR (Now reorganized as MMEditing). Please refer to the original repo for more usage and documents.

[Paper] [Project Page]

Overview

Dependencies and Installation

• Python >= 3.7
• PyTorch == 1.4
• CUDA 10.0
• GCC 5.4.0

1. Install Dependencies

   cd AMSA
   conda install pytorch=1.4.0 torchvision cudatoolkit=10.0 -c pytorch
   pip install mmcv==0.4.4
   pip install -r requirements.txt

2. Install MMSR and DCNv2

   python setup.py develop
   cd mmsr/models/archs/DCNv2
   python setup.py build develop

Dataset Preparation

• Train Set: CUFED Dataset
• Test Set: CUFED5 Dataset

Please refer to Datasets.md for pre-processing and more details.

Get Started

Pretrained Models

Downloading the pretrained models from GoogleDrive and put them under experiments/pretrained_models folder.

Test

We provide quick test code with the pretrained model.

1. Modify the paths to dataset and pretrained model in the following yaml files for configuration.

   ./options/test/test_AMSA.yml
   ./options/test/test_AMSA_mse.yml

2. Run test code for models trained using GAN loss.

   python mmsr/test.py -opt "options/test/test_AMSA.yml"

   Check out the results in ./results.

3. Run test code for models trained using only reconstruction loss.

   python mmsr/test.py -opt "options/test/test_AMSA_mse.yml"

   Check out the results in in ./results

Train

Downloading the pretrained feature extraction models from C2-Matching link and put "feature_extraction.pth" under experiments/pretrained_models folder.

All logging files in the training process, e.g., log message, checkpoints, and snapshots, will be saved to ./experiments and ./tb_logger directory.

1. Train restoration network.

# add the path to *pretrain_model_feature_extractor* in the following yaml
# the path to *pretrain_model_feature_extractor* is the model obtained in C2-Matching
./options/train/stage3_restoration_gan.yml
python mmsr/train.py -opt "options/train/stage3_restoration_gan.yml"

# if you wish to train the restoration network with only mse loss
# prepare the dataset path and pretrained model path in the following yaml
./options/train/stage3_restoration_mse.yml
python mmsr/train.py -opt "options/train/stage3_restoration_mse.yml"

Results

Citation

If you find our repo useful for your research, please consider citing our paper:

@article{xia2022coarse,
title={Coarse-to-Fine Embedded PatchMatch and Multi-Scale Dynamic Aggregation for Reference-based Super-Resolution},
author={Xia, Bin and Tian, Yapeng and Hang, Yucheng and Yang, Wenming and Liao, Qingmin and Zhou, Jie},
booktitle={AAAI},
year={2022}
}