DCSR: Dual Camera Super-Resolution

Implementation for our ICCV 2021 oral paper: Dual-Camera Super-Resolution with Aligned Attention Modules

paper | project website | dataset | demo video

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.