RepECN

This repository is an official PyTorch implementation of the paper "RepECN: Making ConvNets Better again for Efficient Image Super-Resolution" from Sensors (paper, pretrained models, visual results).

We provide scripts for reproducing all the results from our paper. You can train your model from scratch, or use a pre-trained model to enlarge your images.

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Traditional Convolutional Neural Network (ConvNet, CNN)-based image super-resolution (SR) methods have lower computation costs, making them more friendly for real-world scenarios. However, they suffer from lower performance. On the contrary, Vision Transformer (ViT)-based SR methods have achieved impressive performance recently, but these methods often suffer from high computation costs and model storage overhead, making them hard to meet the requirements in practical application scenarios. In practical scenarios, an SR model should reconstruct an image with high quality and fast inference. To handle this issue, we propose a novel CNN-based Efficient Residual ConvNet enhanced with structural Re-parameterization (RepECN) for a better trade-off between performance and efficiency. A stage-to-block hierarchical architecture design paradigm inspired by ViT is utilized to keep the state-of-the-art performance, while the efficiency is ensured by abandoning the time-consuming Multi-Head Self-Attention (MHSA) and by re-designing the block-level modules based on CNN. Specifically, RepECN consists of three structural modules: a shallow feature extraction module, a deep feature extraction, and an image reconstruction module. The deep feature extraction module comprises multiple ConvNet Stages (CNS), each containing 6 Re-Parameterization ConvNet Blocks (RepCNB), a head layer, and a residual connection. The RepCNB utilizes larger kernel convolutions rather than MHSA to enhance the capability of learning long-range dependence. In the image reconstruction module, an upsampling module consisting of nearest-neighbor interpolation and pixel attention is deployed to reduce parameters and maintain reconstruction performance, while bicubic interpolation on another branch allows the backbone network to focus on learning high-frequency information. The extensive experimental results on multiple public benchmarks show that our RepECN can achieve 2.5$\sim$5$\times$ faster inference than the state-of-the-art ViT-based SR model with better or competitive super-resolving performance, indicating that our RepECN can reconstruct high-quality images with fast inference.

Dependencies

• Python 3.6
• PyTorch >= 1.10.0
• numpy
• scikit-image [instead of skimage]
• imageio
• matplotlib
• tqdm
• cv2 >= 3.xx (Only if you want to use video input/output) [should pip install opencv-python]
• torchinfo
• thop
• timm
• mmdet
• pytorch_wavelets

Code

• Clone this repository into any place you want.

git clone https://github.com/qpchen/RepECN
cd RepECN

• Create a conda virtual environment and activate it:

conda create -n repecn python=3.9 -y
conda activate repecn

• Install CUDA>=10.2 with cudnn>=7 following the official installation instructions
• Install PyTorch>=1.10.0 and torchvision>=0.9.0 with CUDA>=10.2 following the official installation instructions:

For examples, to install torch==1.10.1 with CUDA==11.1:

pip install torch==1.10.1+cu111 torchvision==0.11.2+cu111 torchaudio==0.10.1 -f https://download.pytorch.org/whl/cu111/torch_stable.html

• Install timm==0.6.11 and mmcv-full==1.5.0:

pip install -U openmim
mim install mmcv-full==1.5.0
pip install timm==0.6.11 mmdet==2.28.1

• Install other requirements:

pip install opencv-python termcolor yacs pyyaml scipy scikit-image imageio matplotlib tqdm torchinfo thop pytorch_wavelets

Data Preparation

Quickstart (Demo)

You can test our super-resolution algorithm with your images. Place your images in test folder. (like test/<your_image>) We support png and jpeg files.

Run the script in src folder. Before you run the demo, please uncomment the appropriate line in demo.sh that you want to execute.

cd src       # You are now in */RepECN/src
sh demo.sh

You can find the result images from experiment/test/results folder.

You can evaluate your models with widely-used benchmark datasets:

Set5 - Bevilacqua et al. BMVC 2012,

Set14 - Zeyde et al. LNCS 2010,

B100 - Martin et al. ICCV 2001,

Urban100 - Huang et al. CVPR 2015.

For these datasets, we first convert the result images to YCbCr color space and evaluate PSNR on the Y channel only. You can download benchmark datasets (250MB). Set --dir_data <where_benchmark_folder_located> to evaluate the RepECN with the benchmarks.

How to train RepECN

We used DIV2K dataset to train our model. Please download it from here (7.1GB).

Unpack the tar file to any place you want. Then, change the dir_data argument in src/option.py to the place where DIV2K images are located.

We recommend you to pre-process the images before training. This step will decode all png files and save them as binaries. Use --ext sep_reset argument on your first run. You can skip the decoding part and use saved binaries with --ext sep argument.

If you have enough RAM (>= 32GB), you can use --ext bin argument to pack all DIV2K images in one binary file.

You can train RepECN by yourself. All scripts are provided in the src/scripts/train_repecn.sh.

cd src       # You are now in */RepECN/src
# train RepECN (x2) model
./scripts/train_repecn.sh train 0 1 b b 2 48 ms 1acb3 1acb3 batch befln nolr 2e-4 bicubic 0 0
# train RepECN (x3) model
./scripts/train_repecn.sh train 0 1 b b 3 48 ms 1acb3 1acb3 batch befln nolr 2e-4 bicubic 0 0
# train RepECN (x4) model
./scripts/train_repecn.sh train 0 1 b b 4 48 ms 1acb3 1acb3 batch befln nolr 2e-4 bicubic 0 0

Then, you can evaluate the trained model by first switch the ACB block to the conventional convolutional block.

# switch ACB to CNN for trained RepECN (x4) model
./scripts/train_repecn.sh switch 0 1 b b 4 48 ms 1acb3 1acb3 batch befln nolr 2e-4 bicubic 0 0
# test the trained RepECN (x4) model
./scripts/train_repecn.sh eval 0 1 b b 4 48 ms 1acb3 1acb3 batch befln nolr 2e-4 bicubic 0 0

You can also do lam analysis for the scale 4 models.

# do LAM analysis on RepECN (x4) model
./scripts/train_repecn.sh lam 0 1 b b 4 48 ms 1acb3 1acb3 batch befln nolr 2e-4 bicubic 0 0

Citation

@article{chen2023repecn,
  title={RepECN: Making ConvNets Better again for Efficient Image Super-Resolution},
  author={Chen, Qiangpu and Qin, Jinghui and Wen, Wushao},
  journal={Sensors},
  year={2023}
}

License and Acknowledgement

This project is released under the MIT license. The basic framework codes are based on EDSR. The LAM analysis, ACB, and DBB codes are based on FriedRiceLab, ACNet, and DiverseBranchBlock, respectively. Please also follow their licenses. Thanks for their awesome works.