DCSR: Dual Camera Super-Resolution
Implementation for our ICCV 2021 oral paper: Dual-Camera Super-Resolution with Aligned Attention Modules
Introduction
We present a novel approach to reference-based super-resolution (RefSR) with the focus on real-world dual-camera super-resolution (DCSR).
Setup
Installation
git clone https://github.com/Tengfei-Wang/DualCameraSR.git
cd DualCameraSR
Environment
The environment can be simply set up by Anaconda:
conda create -n DCSR python=3.7
conda activate DCSR
pip install -r requirements.txt
Dataset
Download our CameraFusion dataset from this link. This dataset currently consists of 143 pairs of telephoto and wide-angle images in 4K resolution captured by smartphone dual-cameras.
mkdir data
cd ./data
unzip CameraFusion.zip
Quick Start
The pretrained models have been put in ./experiments/pretrain. For quick test, run the scipts:
# For 4K test (with ground-truth High-Resolution images):
sh test.py
# For 8K test (without SRA):
sh test_8k.sh
# For 8K test (with SRA):
sh test_8k_SRA.sh
Training
To train the DCSR model on CameraFusion, run:
sh train.sh
The trained model should perform well on 4K test, but may suffer performance degradation on 8K test.
After the regular training, we can use Self-supervised Real-image Adaptation (SRA) to finetune the trained model for real-world 8K image applications:
sh train_SRA.sh
Citation
If you find this work useful for your research, please cite:
@InProceedings{wang2021DCSR,
author = {Wang, Tengfei and Xie, Jiaxin and Sun, Wenxiu and Yan, Qiong and Chen, Qifeng},
title = {Dual-Camera Super-Resolution with Aligned Attention Modules},
booktitle = {International Conference on Computer Vision (ICCV)},
year = {2021}
}
Acknowledgement
We thank the authors of EDSR, CSNLN, TTSR and style-swap for sharing their codes.
GitHub
Tengfei-Wang
